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[ { "type": "text", "value": "My 1st post on ๐Ÿค— I would love to discuss topics related to bias in LLMs: ", "raw": "My 1st post on ๐Ÿค— I would love to discuss topics related to bias in LLMs: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1) Are researchers and enterprises concerned about detecting and addressing social bias in the Gen AI applications? If so, what are the existing approaches?", "raw": "1) Are researchers and enterprises concerned about detecting and addressing social bias in the Gen AI applications? If so, what are the existing approaches?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2) Are there trusted and labeled datasets to evaluate bias in LLM generations? ", "raw": "2) Are there trusted and labeled datasets to evaluate bias in LLM generations? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
My 1st post on ๐Ÿค— I would love to discuss topics related to bias in LLMs: 1) Are researchers and enterprises concerned about detecting and addressing social bias in the Gen AI applications? If so, what are the existing approaches? 2) Are there trusted and labeled datasets to evaluate bias in LLM generations?
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2024-05-30T22:26:43.000Z
2024-05-30T22:26:43.942Z
[]
/posts/Shivansh000/134000407232815
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[ { "type": "text", "value": "๐Ÿฆ… Falcon has landed... again! ", "raw": "๐Ÿฆ… Falcon has landed... again! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And now it not just reads but sees as well ๐Ÿ“–๐Ÿ‘€", "raw": "And now it not just reads but sees as well ๐Ÿ“–๐Ÿ‘€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here is a summary of the Falcon-11B-VLM model:", "raw": "Here is a summary of the Falcon-11B-VLM model:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model Type: Causal decoder-only model ๐Ÿ”„.", "raw": "Model Type: Causal decoder-only model ๐Ÿ”„.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Parameters: 11 billion ๐ŸŒŒ.", "raw": "Parameters: 11 billion ๐ŸŒŒ.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Vision Integration: Uses the pretrained CLIP ViT-L/14 vision encoder with the recently released Falcon2-11B chat-finetuned model and trained with image-text data ๐Ÿ–ผ๏ธ๐Ÿ“š.", "raw": "Vision Integration: Uses the pretrained CLIP ViT-L/14 vision encoder with the recently released Falcon2-11B chat-finetuned model and trained with image-text data ๐Ÿ–ผ๏ธ๐Ÿ“š.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Training: Pretrained on over 5,000 billion tokens from RefinedWeb with curated corpora ๐Ÿ“Š.", "raw": "Training: Pretrained on over 5,000 billion tokens from RefinedWeb with curated corpora ๐Ÿ“Š.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dynamic Encoding: Enhances perception of fine-grained details in images ๐Ÿ”.", "raw": "Dynamic Encoding: Enhances perception of fine-grained details in images ๐Ÿ”.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Training Hardware: 16 A100 80GB GPUs with ZeRO and Flash-Attention 2 ๐Ÿ–ฅ๏ธ.", "raw": "Training Hardware: 16 A100 80GB GPUs with ZeRO and Flash-Attention 2 ๐Ÿ–ฅ๏ธ.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Tokenizer: Falcon-7B/11B tokenizer ๐Ÿงฉ.", "raw": "Tokenizer: Falcon-7B/11B tokenizer ๐Ÿงฉ.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Languages Supported: ๐ŸŒ Primarily English, with capabilities in German ๐Ÿ‡ฉ๐Ÿ‡ช, Spanish ๐Ÿ‡ช๐Ÿ‡ธ, French ๐Ÿ‡ซ๐Ÿ‡ท, Italian ๐Ÿ‡ฎ๐Ÿ‡น, Dutch ๐Ÿ‡ณ๐Ÿ‡ฑ, Romanian ๐Ÿ‡ท๐Ÿ‡ด, Czech ๐Ÿ‡จ๐Ÿ‡ฟ, Swedish ๐Ÿ‡ธ๐Ÿ‡ช, and more. ๐Ÿ—ฃ๏ธ๐ŸŒ.", "raw": "Languages Supported: ๐ŸŒ Primarily English, with capabilities in German ๐Ÿ‡ฉ๐Ÿ‡ช, Spanish ๐Ÿ‡ช๐Ÿ‡ธ, French ๐Ÿ‡ซ๐Ÿ‡ท, Italian ๐Ÿ‡ฎ๐Ÿ‡น, Dutch ๐Ÿ‡ณ๐Ÿ‡ฑ, Romanian ๐Ÿ‡ท๐Ÿ‡ด, Czech ๐Ÿ‡จ๐Ÿ‡ฟ, Swedish ๐Ÿ‡ธ๐Ÿ‡ช, and more. ๐Ÿ—ฃ๏ธ๐ŸŒ.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "License: Open Source - TII Falcon License 2.0, based on Apache 2.0 ๐Ÿ“œ.", "raw": "License: Open Source - TII Falcon License 2.0, based on Apache 2.0 ๐Ÿ“œ.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/tiiuae/falcon-11B-vlm", "href": null, "resource": { "type": "model", "id": "tiiuae/falcon-11B-vlm", "discussionNum": null }, "url": "https://huggingface.co/tiiuae/falcon-11B-vlm", "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿฆ… Falcon has landed... again! And now it not just reads but sees as well ๐Ÿ“–๐Ÿ‘€ Here is a summary of the Falcon-11B-VLM model: Model Type: Causal decoder-only model ๐Ÿ”„. Parameters: 11 billion ๐ŸŒŒ. Vision Integration: Uses the pretrained CLIP ViT-L/14 vision encoder with the recently released Falcon2-11B chat-finetuned model and trained with image-text data ๐Ÿ–ผ๏ธ๐Ÿ“š. Training: Pretrained on over 5,000 billion tokens from RefinedWeb with curated corpora ๐Ÿ“Š. Dynamic Encoding: Enhances perception of fine-grained details in images ๐Ÿ”. Training Hardware: 16 A100 80GB GPUs with ZeRO and Flash-Attention 2 ๐Ÿ–ฅ๏ธ. Tokenizer: Falcon-7B/11B tokenizer ๐Ÿงฉ. Languages Supported: ๐ŸŒ Primarily English, with capabilities in German ๐Ÿ‡ฉ๐Ÿ‡ช, Spanish ๐Ÿ‡ช๐Ÿ‡ธ, French ๐Ÿ‡ซ๐Ÿ‡ท, Italian ๐Ÿ‡ฎ๐Ÿ‡น, Dutch ๐Ÿ‡ณ๐Ÿ‡ฑ, Romanian ๐Ÿ‡ท๐Ÿ‡ด, Czech ๐Ÿ‡จ๐Ÿ‡ฟ, Swedish ๐Ÿ‡ธ๐Ÿ‡ช, and more. ๐Ÿ—ฃ๏ธ๐ŸŒ. License: Open Source - TII Falcon License 2.0, based on Apache 2.0 ๐Ÿ“œ. Model: https://huggingface.co/tiiuae/falcon-11B-vlm
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2024-05-30T20:47:09.000Z
2024-05-30T20:47:09.155Z
[]
/posts/singhsidhukuldeep/450962887111037
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[ { "type": "text", "value": "You can now train/finetune custom sentence transformer embedding models using AutoTrain. Read blog: ", "raw": "You can now train/finetune custom sentence transformer embedding models using AutoTrain. Read blog: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/abhishek/finetune-custom-embeddings-autotrain", "href": "https://huggingface.co/blog/abhishek/finetune-custom-embeddings-autotrain", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
You can now train/finetune custom sentence transformer embedding models using AutoTrain. Read blog: https://huggingface.co/blog/abhishek/finetune-custom-embeddings-autotrain
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2024-05-30T16:25:08.000Z
2024-06-04T08:47:53.678Z
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/posts/abhishek/938075119628356
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909649839818293
[ { "type": "text", "value": "ChatGPT made Custom GPTs Free for Everyone.", "raw": "ChatGPT made Custom GPTs Free for Everyone.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Yes, you can use them but...", "raw": "Yes, you can use them but...", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "with limitations like", "raw": "with limitations like", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can't use DallE ๐Ÿ˜ฅ, ", "raw": "You can't use DallE ๐Ÿ˜ฅ, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can't make Custom GPTs ", "raw": "You can't make Custom GPTs ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And chat limit also๐Ÿ˜ฅ.", "raw": "And chat limit also๐Ÿ˜ฅ.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "But...", "raw": "But...", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We already have an open-source alternative like Hugging Chat, where you can create your custom assistant, generate, edit images, without any chat limit.", "raw": "We already have an open-source alternative like Hugging Chat, where you can create your custom assistant, generate, edit images, without any chat limit.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Try both of them from here:", "raw": "Try both of them from here:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://chatgpt.com/gpts", "href": "https://chatgpt.com/gpts", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/chat", "href": "https://huggingface.co/chat", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "and don't forget to Give your review here ๐Ÿ‘‡:", "raw": "and don't forget to Give your review here ๐Ÿ‘‡:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
ChatGPT made Custom GPTs Free for Everyone. Yes, you can use them but... with limitations like You can't use DallE ๐Ÿ˜ฅ, You can't make Custom GPTs And chat limit also๐Ÿ˜ฅ. But... We already have an open-source alternative like Hugging Chat, where you can create your custom assistant, generate, edit images, without any chat limit. Try both of them from here: https://chatgpt.com/gpts https://huggingface.co/chat and don't forget to Give your review here ๐Ÿ‘‡:
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2024-05-30T15:12:05.000Z
2024-11-04T21:13:50.769Z
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/posts/KingNish/909649839818293
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[ { "type": "text", "value": "I ran 580 experiments (yes, 580 ๐Ÿคฏ) to check if we can quantify data drift's impact on model performance using only drift metrics.", "raw": "I ran 580 experiments (yes, 580 ๐Ÿคฏ) to check if we can quantify data drift's impact on model performance using only drift metrics.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For these experiments, I built a technique that relies on drift signals to estimate model performance. I compared its results against the current SoTA performance estimation methods and checked which technique performs best.", "raw": "For these experiments, I built a technique that relies on drift signals to estimate model performance. I compared its results against the current SoTA performance estimation methods and checked which technique performs best.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The plot below summarizes the general results. It measures the quality of performance estimation versus the absolute performance change. (The lower, the better).", "raw": "The plot below summarizes the general results. It measures the quality of performance estimation versus the absolute performance change. (The lower, the better).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Full experiment: ", "raw": "Full experiment: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.nannyml.com/blog/data-drift-estimate-model-performance", "href": "https://www.nannyml.com/blog/data-drift-estimate-model-performance", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In it, I describe the setup, datasets, models, benchmarking methods, and the code used in the project.", "raw": "In it, I describe the setup, datasets, models, benchmarking methods, and the code used in the project.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I ran 580 experiments (yes, 580 ๐Ÿคฏ) to check if we can quantify data drift's impact on model performance using only drift metrics. For these experiments, I built a technique that relies on drift signals to estimate model performance. I compared its results against the current SoTA performance estimation methods and checked which technique performs best. The plot below summarizes the general results. It measures the quality of performance estimation versus the absolute performance change. (The lower, the better). Full experiment: https://www.nannyml.com/blog/data-drift-estimate-model-performance In it, I describe the setup, datasets, models, benchmarking methods, and the code used in the project.
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2024-05-30T15:05:48.000Z
2024-05-30T15:05:48.986Z
[]
/posts/santiviquez/630726145653856
1,567
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[ { "type": "text", "value": "Jamba GGUF!", "raw": "Jamba GGUF!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Finally, thanks to the awesome work of the brilliant mind of Github user compilade (", "raw": "Finally, thanks to the awesome work of the brilliant mind of Github user compilade (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/compilade", "href": "https://github.com/compilade", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") Jamba is now beginning to be supported in llama.cpp (just CPU inference at the moment). So far there are a few different versions I have been able to convert, mainly the Jamba-Bagel, Jamba-Claude, 900M Jamba-Small and a 1B Jamba", "raw": ") Jamba is now beginning to be supported in llama.cpp (just CPU inference at the moment). So far there are a few different versions I have been able to convert, mainly the Jamba-Bagel, Jamba-Claude, 900M Jamba-Small and a 1B Jamba", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/Severian/jamba-gguf-665884eb2ceef24c1a0547e0", "href": null, "resource": { "type": "collection", "id": "Severian/jamba-gguf-665884eb2ceef24c1a0547e0", "discussionNum": null }, "url": "https://huggingface.co/collections/Severian/jamba-gguf-665884eb2ceef24c1a0547e0", "code": null, "user": null, "label": null, "lang": null } ]
Jamba GGUF! Finally, thanks to the awesome work of the brilliant mind of Github user compilade (https://github.com/compilade) Jamba is now beginning to be supported in llama.cpp (just CPU inference at the moment). So far there are a few different versions I have been able to convert, mainly the Jamba-Bagel, Jamba-Claude, 900M Jamba-Small and a 1B Jamba https://huggingface.co/collections/Severian/jamba-gguf-665884eb2ceef24c1a0547e0
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2024-05-30T13:57:57.000Z
2024-05-30T13:57:57.143Z
[]
/posts/Severian/281477644792175
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714627846790266
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We explore extremely low-weight merger as an alternative to fine-tuning; e.g., weight 1e-4. Merge formula details here: https://huggingface.co/grimjim/kukulemon-v3-soul_mix-32k-7B
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2024-05-30T12:28:15.000Z
2024-06-25T04:06:55.700Z
[]
/posts/grimjim/714627846790266
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[ { "type": "text", "value": "Started a new AI Session: The AI Paper Talk Show ๐Ÿง ๐Ÿค–๐Ÿ’ฅ", "raw": "Started a new AI Session: The AI Paper Talk Show ๐Ÿง ๐Ÿค–๐Ÿ’ฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In this episode we went through AnthropicAI's recent interpretability paper \"Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet\" in which they applied Sparse Dictionary Learning on a larger model (Claude 3 Sonnet) - wherein they match patterns of neuron activations (named Features) to human interpretable meanings. ", "raw": "In this episode we went through AnthropicAI's recent interpretability paper \"Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet\" in which they applied Sparse Dictionary Learning on a larger model (Claude 3 Sonnet) - wherein they match patterns of neuron activations (named Features) to human interpretable meanings. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check full video here: ", "raw": "Check full video here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/uNz-Ww3_LrU?si=HUm2TWV-rSJ3X4UX", "href": "https://youtu.be/uNz-Ww3_LrU?si=HUm2TWV-rSJ3X4UX", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read More: ", "raw": "Read More: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://transformer-circuits.pub/2024/scaling-monosemanticity/", "href": "https://transformer-circuits.pub/2024/scaling-monosemanticity/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can also find me:", "raw": "You can also find me:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Twitter: ", "raw": "Twitter: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://x.com/jaykef_", "href": "https://x.com/jaykef_", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Github: ", "raw": "Github: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/Jaykef", "href": "https://github.com/Jaykef", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Started a new AI Session: The AI Paper Talk Show ๐Ÿง ๐Ÿค–๐Ÿ’ฅ In this episode we went through AnthropicAI's recent interpretability paper "Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet" in which they applied Sparse Dictionary Learning on a larger model (Claude 3 Sonnet) - wherein they match patterns of neuron activations (named Features) to human interpretable meanings. Check full video here: https://youtu.be/uNz-Ww3_LrU?si=HUm2TWV-rSJ3X4UX Read More: https://transformer-circuits.pub/2024/scaling-monosemanticity/ You can also find me: Twitter: https://x.com/jaykef_ Github: https://github.com/Jaykef
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[]
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2024-05-30T12:20:03.000Z
2024-05-31T13:38:14.724Z
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/posts/Jaward/337171791133693
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2
684244223607541
[ { "type": "text", "value": "I will be delivering an introductory coding session this Sunday 7Pm gmt+1 time about huggingface, if you are new to HF and don't know where to begin, you are welcome to join us ๐Ÿค—", "raw": "I will be delivering an introductory coding session this Sunday 7Pm gmt+1 time about huggingface, if you are new to HF and don't know where to begin, you are welcome to join us ๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“ŒPlace: huggingface discord server ", "raw": "๐Ÿ“ŒPlace: huggingface discord server ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”—Link : ", "raw": "๐Ÿ”—Link : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discord.gg/hugging-face-879548962464493619?event=1245406127668203541", "href": "https://discord.gg/hugging-face-879548962464493619?event=1245406127668203541", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I will be delivering an introductory coding session this Sunday 7Pm gmt+1 time about huggingface, if you are new to HF and don't know where to begin, you are welcome to join us ๐Ÿค— ๐Ÿ“ŒPlace: huggingface discord server ๐Ÿ”—Link : https://discord.gg/hugging-face-879548962464493619?event=1245406127668203541
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[]
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2024-05-30T09:50:04.000Z
2024-05-30T17:17:00.627Z
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/posts/not-lain/684244223607541
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598209849882428
[ { "type": "text", "value": "Do you need a high-quality dataset to train a custom sentence transformer model? Look no further! I've developed a pipeline that leverages an LLM to create a synthetic dataset of negative and positive sentence pairs based on domain-specific anchors.", "raw": "Do you need a high-quality dataset to train a custom sentence transformer model? Look no further! I've developed a pipeline that leverages an LLM to create a synthetic dataset of negative and positive sentence pairs based on domain-specific anchors.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here's what the pipeline offers:", "raw": "Here's what the pipeline offers:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- **Dataset Generation**: Automatically create synthetic sentence pairs ", "raw": "- **Dataset Generation**: Automatically create synthetic sentence pairs ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- **Mine hard negatives**: Use an existing embedding model to mine hard negatives ", "raw": "- **Mine hard negatives**: Use an existing embedding model to mine hard negatives ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- **Model Training**: Train a model using the latest release of Sentence Transformers.", "raw": "- **Model Training**: Train a model using the latest release of Sentence Transformers.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check out this collection (", "raw": "Check out this collection (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/davanstrien/sentence-transformers-from-synthetic-data-66571a6133480d1b70066b70", "href": null, "resource": { "type": "collection", "id": "davanstrien/sentence-transformers-from-synthetic-data-66571a6133480d1b70066b70", "discussionNum": null }, "url": "https://huggingface.co/collections/davanstrien/sentence-transformers-from-synthetic-data-66571a6133480d1b70066b70", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") to see an example of what you can achieve with this pipeline. It features a sentence transformer model to detect coding prompt similarities in a ", "raw": ") to see an example of what you can achieve with this pipeline. It features a sentence transformer model to detect coding prompt similarities in a ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@bigcode", "href": null, "resource": null, "url": null, "code": null, "user": "bigcode", "label": null, "lang": null }, { "type": "text", "value": " dataset. ", "raw": " dataset. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Excited to get started? Find a tutorial here: ", "raw": "Excited to get started? Find a tutorial here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/davanstrien/awesome-synthetic-datasets/tree/main/examples/embedding-datasets", "href": "https://github.com/davanstrien/awesome-synthetic-datasets/tree/main/examples/embedding-datasets", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ". ", "raw": ". ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Do you need a high-quality dataset to train a custom sentence transformer model? Look no further! I've developed a pipeline that leverages an LLM to create a synthetic dataset of negative and positive sentence pairs based on domain-specific anchors. Here's what the pipeline offers: - **Dataset Generation**: Automatically create synthetic sentence pairs - **Mine hard negatives**: Use an existing embedding model to mine hard negatives - **Model Training**: Train a model using the latest release of Sentence Transformers. Check out this collection (https://huggingface.co/collections/davanstrien/sentence-transformers-from-synthetic-data-66571a6133480d1b70066b70) to see an example of what you can achieve with this pipeline. It features a sentence transformer model to detect coding prompt similarities in a @bigcode dataset. Excited to get started? Find a tutorial here: https://github.com/davanstrien/awesome-synthetic-datasets/tree/main/examples/embedding-datasets.
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2024-05-30T08:14:00.000Z
2024-05-30T11:11:21.861Z
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/posts/davanstrien/598209849882428
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[ { "type": "text", "value": "Do we fully leverage ViT encoders in vision language models? ", "raw": "Do we fully leverage ViT encoders in vision language models? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A new paper (by ", "raw": "A new paper (by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@HuanjinYao", "href": null, "resource": null, "url": null, "code": null, "user": "HuanjinYao", "label": null, "lang": null }, { "type": "text", "value": " et al) built a dense connector that does it better! ", "raw": " et al) built a dense connector that does it better! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/HuanjinYao/DenseConnector-v1.5-8B", "href": null, "resource": { "type": "space", "id": "HuanjinYao/DenseConnector-v1.5-8B", "discussionNum": null }, "url": "https://huggingface.co/spaces/HuanjinYao/DenseConnector-v1.5-8B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/HuanjinYao/denseconnector-66500e173fc8c9f05dc98dea", "href": null, "resource": { "type": "collection", "id": "HuanjinYao/denseconnector-66500e173fc8c9f05dc98dea", "discussionNum": null }, "url": "https://huggingface.co/collections/HuanjinYao/denseconnector-66500e173fc8c9f05dc98dea", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "VLMs consist of an image encoder block, a projection layer that projects image embeddings to text embedding space and then a text decoder sequentially connected ๐Ÿ“–", "raw": "VLMs consist of an image encoder block, a projection layer that projects image embeddings to text embedding space and then a text decoder sequentially connected ๐Ÿ“–", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This paper explores using intermediate states of image encoder and not a single output ๐Ÿคฉ", "raw": "This paper explores using intermediate states of image encoder and not a single output ๐Ÿคฉ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The authors explore three different ways of instantiating dense connector: sparse token integration, sparse channel integration and dense channel integration. (see paper on how they do it ", "raw": "The authors explore three different ways of instantiating dense connector: sparse token integration, sparse channel integration and dense channel integration. (see paper on how they do it ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2405.13800", "href": null, "resource": { "type": "paper", "id": "2405.13800", "discussionNum": null }, "url": "https://huggingface.co/papers/2405.13800", "code": null, "user": null, "label": "Dense Connector for MLLMs (2405.13800)", "lang": null }, { "type": "text", "value": ")", "raw": ")", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "They explore all three of them integrated to LLaVA 1.5 and found out each of the new models are superior to the original LLaVA 1.5 ๐Ÿฅน I tried the model and it seems to work very well. As part of the release, the authors have released various ckpts based on different decoders (Vicuna 7/13B and Llama 3-8B) that you can find in the collection ๐Ÿค— ", "raw": "They explore all three of them integrated to LLaVA 1.5 and found out each of the new models are superior to the original LLaVA 1.5 ๐Ÿฅน I tried the model and it seems to work very well. As part of the release, the authors have released various ckpts based on different decoders (Vicuna 7/13B and Llama 3-8B) that you can find in the collection ๐Ÿค— ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Do we fully leverage ViT encoders in vision language models? A new paper (by @HuanjinYao et al) built a dense connector that does it better! https://huggingface.co/spaces/HuanjinYao/DenseConnector-v1.5-8B https://huggingface.co/collections/HuanjinYao/denseconnector-66500e173fc8c9f05dc98dea VLMs consist of an image encoder block, a projection layer that projects image embeddings to text embedding space and then a text decoder sequentially connected ๐Ÿ“– This paper explores using intermediate states of image encoder and not a single output ๐Ÿคฉ The authors explore three different ways of instantiating dense connector: sparse token integration, sparse channel integration and dense channel integration. (see paper on how they do it https://huggingface.co/papers/2405.13800) They explore all three of them integrated to LLaVA 1.5 and found out each of the new models are superior to the original LLaVA 1.5 ๐Ÿฅน I tried the model and it seems to work very well. As part of the release, the authors have released various ckpts based on different decoders (Vicuna 7/13B and Llama 3-8B) that you can find in the collection ๐Ÿค—
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2024-05-30T08:11:18.000Z
2024-05-30T08:11:18.967Z
[]
/posts/merve/663415823502048
2,060
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535648953516791
[ { "type": "text", "value": "WorkerSafetyQAEval: A new benchmark to evaluate worker safety domain question and answering", "raw": "WorkerSafetyQAEval: A new benchmark to evaluate worker safety domain question and answering", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Happy to share a new benchmark on question and answers for worker safety domain. The benchmark and leaderboard is available at ", "raw": "Happy to share a new benchmark on question and answers for worker safety domain. The benchmark and leaderboard is available at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/codelion/worker-safety-qa-eval", "href": null, "resource": { "type": "dataset", "id": "codelion/worker-safety-qa-eval", "discussionNum": null }, "url": "https://huggingface.co/datasets/codelion/worker-safety-qa-eval", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We evaluate popular generic chatbots like ChatGPT and HuggingChat on WorkerSafetyQAEval and compare it with a domain specific RAG bot called Securade.ai Safety Copilot - ", "raw": "We evaluate popular generic chatbots like ChatGPT and HuggingChat on WorkerSafetyQAEval and compare it with a domain specific RAG bot called Securade.ai Safety Copilot - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/codelion/safety-copilot", "href": null, "resource": { "type": "space", "id": "codelion/safety-copilot", "discussionNum": null }, "url": "https://huggingface.co/spaces/codelion/safety-copilot", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " It highlights the importance of having domain specific knowledge for critical domains like worker safety that require high accuracy. Securade.ai Safety Copilot achieves ~97% on the benchmark setting a new SOTA.", "raw": " It highlights the importance of having domain specific knowledge for critical domains like worker safety that require high accuracy. Securade.ai Safety Copilot achieves ~97% on the benchmark setting a new SOTA.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can read more about the Safety Copilot on ", "raw": "You can read more about the Safety Copilot on ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://securade.ai/blog/how-securade-ai-safety-copilot-transforms-worker-safety.html", "href": "https://securade.ai/blog/how-securade-ai-safety-copilot-transforms-worker-safety.html", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
WorkerSafetyQAEval: A new benchmark to evaluate worker safety domain question and answering Happy to share a new benchmark on question and answers for worker safety domain. The benchmark and leaderboard is available at https://huggingface.co/datasets/codelion/worker-safety-qa-eval We evaluate popular generic chatbots like ChatGPT and HuggingChat on WorkerSafetyQAEval and compare it with a domain specific RAG bot called Securade.ai Safety Copilot - https://huggingface.co/spaces/codelion/safety-copilot It highlights the importance of having domain specific knowledge for critical domains like worker safety that require high accuracy. Securade.ai Safety Copilot achieves ~97% on the benchmark setting a new SOTA. You can read more about the Safety Copilot on https://securade.ai/blog/how-securade-ai-safety-copilot-transforms-worker-safety.html
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2024-05-30T04:03:02.000Z
2024-05-30T04:04:53.284Z
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/posts/codelion/535648953516791
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[ { "type": "text", "value": "Phased Consistency Model", "raw": "Phased Consistency Model", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2405.18407", "href": null, "resource": { "type": "paper", "id": "2405.18407", "discussionNum": null }, "url": "https://huggingface.co/papers/2405.18407", "code": null, "user": null, "label": "Phased Consistency Model (2405.18407)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The consistency model (CM) has recently made significant progress in accelerating the generation of diffusion models. However, its application to high-resolution, text-conditioned image generation in the latent space (a.k.a., LCM) remains unsatisfactory. In this paper, we identify three key flaws in the current design of LCM. We investigate the reasons behind these limitations and propose the Phased Consistency Model (PCM), which generalizes the design space and addresses all identified limitations. Our evaluations demonstrate that PCM significantly outperforms LCM across 1--16 step generation settings. While PCM is specifically designed for multi-step refinement, it achieves even superior or comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show that PCM's methodology is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator. ", "raw": "The consistency model (CM) has recently made significant progress in accelerating the generation of diffusion models. However, its application to high-resolution, text-conditioned image generation in the latent space (a.k.a., LCM) remains unsatisfactory. In this paper, we identify three key flaws in the current design of LCM. We investigate the reasons behind these limitations and propose the Phased Consistency Model (PCM), which generalizes the design space and addresses all identified limitations. Our evaluations demonstrate that PCM significantly outperforms LCM across 1--16 step generation settings. While PCM is specifically designed for multi-step refinement, it achieves even superior or comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show that PCM's methodology is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Phased Consistency Model https://huggingface.co/papers/2405.18407 The consistency model (CM) has recently made significant progress in accelerating the generation of diffusion models. However, its application to high-resolution, text-conditioned image generation in the latent space (a.k.a., LCM) remains unsatisfactory. In this paper, we identify three key flaws in the current design of LCM. We investigate the reasons behind these limitations and propose the Phased Consistency Model (PCM), which generalizes the design space and addresses all identified limitations. Our evaluations demonstrate that PCM significantly outperforms LCM across 1--16 step generation settings. While PCM is specifically designed for multi-step refinement, it achieves even superior or comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show that PCM's methodology is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator.
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2024-05-29T22:15:35.000Z
2024-05-29T22:15:35.472Z
[]
/posts/akhaliq/954200692919621
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369925278582072
[ { "type": "text", "value": "You are happy that ", "raw": "You are happy that ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@mistralai", "href": null, "resource": null, "url": null, "code": null, "user": "mistralai", "label": null, "lang": null }, { "type": "text", "value": " is releasing a new model ๐Ÿ˜Š", "raw": " is releasing a new model ๐Ÿ˜Š", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You become even more happy to see it's a completely new coding model ๐Ÿ˜„", "raw": "You become even more happy to see it's a completely new coding model ๐Ÿ˜„", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Then you become sad because the model is licensed under MNLP ๐Ÿ˜”", "raw": "Then you become sad because the model is licensed under MNLP ๐Ÿ˜”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Before we talk about MNLP, here is the gist of the model:", "raw": "Before we talk about MNLP, here is the gist of the model:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿท๏ธName: Codestral (Code + Mistral ๐Ÿ˜‚)", "raw": "๐Ÿท๏ธName: Codestral (Code + Mistral ๐Ÿ˜‚)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ 22B parameters", "raw": "๐Ÿš€ 22B parameters", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŒ Supports 80 programming languages (including Python, Java, C, C++, bash, swift, and more)", "raw": "๐ŸŒ Supports 80 programming languages (including Python, Java, C, C++, bash, swift, and more)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ† Outperforms Llama 3 70B and Code Llama 70B on HumanEval and MBPP", "raw": "๐Ÿ† Outperforms Llama 3 70B and Code Llama 70B on HumanEval and MBPP", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ† Outperforms DeepSeek Coder 33B on HumanEval", "raw": "๐Ÿ† Outperforms DeepSeek Coder 33B on HumanEval", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“œ 32K context window (longer than Llama 3, DeepSeek, or Code Llama)", "raw": "๐Ÿ“œ 32K context window (longer than Llama 3, DeepSeek, or Code Llama)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค– Supports both code assistant and code completion use cases", "raw": "๐Ÿค– Supports both code assistant and code completion use cases", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "More details: ", "raw": "More details: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://mistral.ai/news/codestral/", "href": "https://mistral.ai/news/codestral/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/mistralai/Codestral-22B-v0.1", "href": null, "resource": { "type": "model", "id": "mistralai/Codestral-22B-v0.1", "discussionNum": null }, "url": "https://huggingface.co/mistralai/Codestral-22B-v0.1", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now what's MNLP? It's a non-commercial license for Mistral models that Codestral is released under! More here: ", "raw": "Now what's MNLP? It's a non-commercial license for Mistral models that Codestral is released under! More here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://mistral.ai/news/mistral-ai-non-production-license-mnpl/", "href": "https://mistral.ai/news/mistral-ai-non-production-license-mnpl/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Don't be sad... ๐Ÿ˜ƒ There is another model that's open source and actually gives better performance on HumanEval: ", "raw": "Don't be sad... ๐Ÿ˜ƒ There is another model that's open source and actually gives better performance on HumanEval: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Bin12345/AutoCoder", "href": null, "resource": { "type": "model", "id": "Bin12345/AutoCoder", "discussionNum": null }, "url": "https://huggingface.co/Bin12345/AutoCoder", "code": null, "user": null, "label": null, "lang": null } ]
You are happy that @mistralai is releasing a new model ๐Ÿ˜Š You become even more happy to see it's a completely new coding model ๐Ÿ˜„ Then you become sad because the model is licensed under MNLP ๐Ÿ˜” Before we talk about MNLP, here is the gist of the model: ๐Ÿท๏ธName: Codestral (Code + Mistral ๐Ÿ˜‚) ๐Ÿš€ 22B parameters ๐ŸŒ Supports 80 programming languages (including Python, Java, C, C++, bash, swift, and more) ๐Ÿ† Outperforms Llama 3 70B and Code Llama 70B on HumanEval and MBPP ๐Ÿ† Outperforms DeepSeek Coder 33B on HumanEval ๐Ÿ“œ 32K context window (longer than Llama 3, DeepSeek, or Code Llama) ๐Ÿค– Supports both code assistant and code completion use cases More details: https://mistral.ai/news/codestral/ https://huggingface.co/mistralai/Codestral-22B-v0.1 Now what's MNLP? It's a non-commercial license for Mistral models that Codestral is released under! More here: https://mistral.ai/news/mistral-ai-non-production-license-mnpl/ Don't be sad... ๐Ÿ˜ƒ There is another model that's open source and actually gives better performance on HumanEval: https://huggingface.co/Bin12345/AutoCoder
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2024-05-29T20:45:04.000Z
2024-05-29T20:45:04.646Z
[]
/posts/singhsidhukuldeep/369925278582072
1,476
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[ { "type": "text", "value": "What We Learned from a Year of Building with LLMs", "raw": "What We Learned from a Year of Building with LLMs", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It's a nice perspective outlined in here. ", "raw": "It's a nice perspective outlined in here. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โ€œWhen a measure becomes a target, it ceases to be a good measure.โ€", "raw": "โ€œWhen a measure becomes a target, it ceases to be a good measure.โ€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โ€” Goodhartโ€™s Law", "raw": "โ€” Goodhartโ€™s Law", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/", "href": "https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
What We Learned from a Year of Building with LLMs It's a nice perspective outlined in here. โ€œWhen a measure becomes a target, it ceases to be a good measure.โ€ โ€” Goodhartโ€™s Law https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/
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2024-05-29T20:04:00.000Z
2024-05-29T20:04:00.655Z
[]
/posts/KnutJaegersberg/418878885559050
1,556
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https://huggingface.co/spaces/nroggendorff/epicrealismxl is growing so fast! thanks guys! ๐Ÿค—
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2024-05-29T18:13:58.000Z
2024-05-29T18:13:58.136Z
[]
/posts/nroggendorff/522377383169636
1,197
0
191311655065773
[ { "type": "text", "value": "๐Ÿ˜ Hello, there are a couple of interesting things. The first is that I will soon release several pretty cool SDXL models, the second is a little sad, I conducted long-term tests of training and merging of XL models and realized that XL will not improve soon, the architecture will not allow us to continue pushing realism and other interesting things into it, the entire community has brought XL closer to the maximum ideal on its architecture.", "raw": "๐Ÿ˜ Hello, there are a couple of interesting things. The first is that I will soon release several pretty cool SDXL models, the second is a little sad, I conducted long-term tests of training and merging of XL models and realized that XL will not improve soon, the architecture will not allow us to continue pushing realism and other interesting things into it, the entire community has brought XL closer to the maximum ideal on its architecture.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ˜ Hello, there are a couple of interesting things. The first is that I will soon release several pretty cool SDXL models, the second is a little sad, I conducted long-term tests of training and merging of XL models and realized that XL will not improve soon, the architecture will not allow us to continue pushing realism and other interesting things into it, the entire community has brought XL closer to the maximum ideal on its architecture.
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2024-05-29T17:19:15.000Z
2024-05-29T17:19:15.666Z
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/posts/ehristoforu/191311655065773
2,129
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[ { "type": "text", "value": "๐Ÿš€๐ŸŽญ๐ŸŒŸ New Research Alert - CVPR 2024 (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿš€", "raw": "๐Ÿš€๐ŸŽญ๐ŸŒŸ New Research Alert - CVPR 2024 (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“„ Title: Relightable Gaussian Codec Avatars ๐Ÿ”", "raw": "๐Ÿ“„ Title: Relightable Gaussian Codec Avatars ๐Ÿ”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“ Description: Relightable Gaussian Codec Avatars is a method for creating highly detailed and relightable 3D head avatars that can animate expressions in real time and support complex features such as hair and skin with efficient rendering suitable for VR.", "raw": "๐Ÿ“ Description: Relightable Gaussian Codec Avatars is a method for creating highly detailed and relightable 3D head avatars that can animate expressions in real time and support complex features such as hair and skin with efficient rendering suitable for VR.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘ฅ Authors: ", "raw": "๐Ÿ‘ฅ Authors: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@psyth", "href": null, "resource": null, "url": null, "code": null, "user": "psyth", "label": null, "lang": null }, { "type": "text", "value": ", ", "raw": ", ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@GBielXONE02", "href": null, "resource": null, "url": null, "code": null, "user": "GBielXONE02", "label": null, "lang": null }, { "type": "text", "value": ", Tomas Simon, Junxuan Li, and ", "raw": ", Tomas Simon, Junxuan Li, and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@giljoonam", "href": null, "resource": null, "url": null, "code": null, "user": "giljoonam", "label": 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"code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://shunsukesaito.github.io/rgca/", "href": "https://shunsukesaito.github.io/rgca/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ CVPR-2023-24-Papers: ", "raw": "๐Ÿš€ CVPR-2023-24-Papers: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/DmitryRyumin/CVPR-2023-24-Papers", "href": "https://github.com/DmitryRyumin/CVPR-2023-24-Papers", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "raw": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "href": null, "resource": { "type": "space", "id": "DmitryRyumin/NewEraAI-Papers", "discussionNum": null }, "url": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " curated by ", "raw": " curated by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@DmitryRyumin", "href": null, "resource": null, "url": null, "code": null, "user": "DmitryRyumin", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ Added to the Avatars Collection: ", "raw": "๐Ÿš€ Added to the Avatars Collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "href": null, "resource": { "type": "collection", "id": "DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "discussionNum": null }, "url": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ” Keywords: #3DAvatars #RealTimeRendering #RelightableAvatars #3DModeling #VirtualReality #CVPR2024 #DeepLearning #ComputerGraphics #ComputerVision #Innovation #VR", "raw": "๐Ÿ” Keywords: #3DAvatars #RealTimeRendering #RelightableAvatars #3DModeling #VirtualReality #CVPR2024 #DeepLearning #ComputerGraphics #ComputerVision #Innovation #VR", "href": null, "resource": null, "url": 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๐Ÿš€๐ŸŽญ๐ŸŒŸ New Research Alert - CVPR 2024 (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿš€ ๐Ÿ“„ Title: Relightable Gaussian Codec Avatars ๐Ÿ” ๐Ÿ“ Description: Relightable Gaussian Codec Avatars is a method for creating highly detailed and relightable 3D head avatars that can animate expressions in real time and support complex features such as hair and skin with efficient rendering suitable for VR. ๐Ÿ‘ฅ Authors: @psyth, @GBielXONE02, Tomas Simon, Junxuan Li, and @giljoonam ๐Ÿ“… Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐Ÿ‡บ๐Ÿ‡ธ ๐Ÿ“„ Paper: https://huggingface.co/papers/2312.03704 ๐ŸŒ GitHub Page: https://shunsukesaito.github.io/rgca/ ๐Ÿš€ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers ๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin ๐Ÿš€ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36 ๐Ÿ” Keywords: #3DAvatars #RealTimeRendering #RelightableAvatars #3DModeling #VirtualReality #CVPR2024 #DeepLearning #ComputerGraphics #ComputerVision #Innovation #VR
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2024-05-29T13:37:32.000Z
2024-05-29T13:37:32.507Z
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/posts/DmitryRyumin/998191475426468
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Introducing Image Generator Pro https://huggingface.co/spaces/KingNish/Image-Gen-Pro It is Expert in Text to Image generation, Sequential Image generation or Image Editing. Examples:
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2024-05-29T12:25:57.000Z
2024-05-29T17:42:50.231Z
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/posts/KingNish/503773999652803
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[ { "type": "text", "value": "# HelpingAI 9B: Cutting Edge Emotionally Intelligent AI", "raw": "# HelpingAI 9B: Cutting Edge Emotionally Intelligent AI", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If you have ever felt that AI not understand your emotions or you not get human like fell while taking to him than this blog is for you!", "raw": "If you have ever felt that AI not understand your emotions or you not get human like fell while taking to him than this blog is for you!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In this BlogPost we will be exploring [HelpingAI 9B](", "raw": "In this BlogPost we will be exploring [HelpingAI 9B](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B", "href": "https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") is an Highly Emotionally Intelligent AI which beated all top notch ai like GPT4o, GPT4, Claude3 Opus on EQ-Bench. ", "raw": ") is an Highly Emotionally Intelligent AI which beated all top notch ai like GPT4o, GPT4, Claude3 Opus on EQ-Bench. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "## What is HelpingAI 9B?", "raw": "## What is HelpingAI 9B?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "![image/png](", "raw": "![image/png](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://cdn-uploads.huggingface.co/production/uploads/6612aedf09f16e7347dfa7e1/FrNhr3WhMhvvD-dNplHxZ.png", "href": "https://cdn-uploads.huggingface.co/production/uploads/6612aedf09f16e7347dfa7e1/FrNhr3WhMhvvD-dNplHxZ.png", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ")", "raw": ")", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "HelpingAI-9B is the fine-tuned Llama2 model crafted for emotionally intelligent conversations. This model excels in empathetic engagement, offering understanding and support through dialogue spanning various topics and situations. Its goal is to serve as a supportive AI companion, adept at resonating with users' emotions and communication requirements.", "raw": "HelpingAI-9B is the fine-tuned Llama2 model crafted for emotionally intelligent conversations. This model excels in empathetic engagement, offering understanding and support through dialogue spanning various topics and situations. Its goal is to serve as a supportive AI companion, adept at resonating with users' emotions and communication requirements.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "## Method", "raw": "## Method", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We gathered a large volume of high-quality human chat data, which was then filtered and refined to create three types of datasets:", "raw": "We gathered a large volume of high-quality human chat data, which was then filtered and refined to create three types of datasets:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. DPOย - Initially, we trained the AI on a substantial DPO dataset to grasp human conversation patterns, enabling it to discern which types of output to generate and which to avoid.", "raw": "1. DPOย - Initially, we trained the AI on a substantial DPO dataset to grasp human conversation patterns, enabling it to discern which types of output to generate and which to avoid.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. Alpacaย - Subsequently, we trained it on an Alpaca-type dataset to enhance its human-like responses.", "raw": "2. Alpacaย - Subsequently, we trained it on an Alpaca-type dataset to enhance its human-like responses.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3. SFTย - Finally, once the AI fully comprehended human interactions, we trained it on an SFT dataset to broaden its knowledge base.", "raw": "3. SFTย - Finally, once the AI fully comprehended human interactions, we trained it on an SFT dataset to broaden its knowledge base.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "## Evaluation", "raw": "## Evaluation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "![By/KingNish/Banchmark/png](", "raw": "![By/KingNish/Banchmark/png](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://cdn-uploads.huggingface.co/production/uploads/6612aedf09f16e7347dfa7e1/xvS57q5kU9f3AX-O-al0k.png", "href": "https://cdn-uploads.huggingface.co/production/uploads/6612aedf09f16e7347dfa7e1/xvS57q5kU9f3AX-O-al0k.png", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ")", "raw": ")", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "## Conclusion:", "raw": "## Conclusion:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "HelpingAI is a large step toward understanding human emoions and give response like them. It can help us lot in making AI better for NLP.", "raw": "HelpingAI is a large step toward understanding human emoions and give response like them. It can help us lot in making AI better for NLP.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Thanks!", "raw": "Thanks!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model Link: - ", "raw": "Model Link: - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/OEvortex/HelpingAI-9B", "href": null, "resource": { "type": "model", "id": "OEvortex/HelpingAI-9B", "discussionNum": null }, "url": "https://huggingface.co/OEvortex/HelpingAI-9B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demo link: - ", "raw": "Demo link: - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B", "href": "https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
# HelpingAI 9B: Cutting Edge Emotionally Intelligent AI If you have ever felt that AI not understand your emotions or you not get human like fell while taking to him than this blog is for you! In this BlogPost we will be exploring [HelpingAI 9B](https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B) is an Highly Emotionally Intelligent AI which beated all top notch ai like GPT4o, GPT4, Claude3 Opus on EQ-Bench. ## What is HelpingAI 9B? ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6612aedf09f16e7347dfa7e1/FrNhr3WhMhvvD-dNplHxZ.png) HelpingAI-9B is the fine-tuned Llama2 model crafted for emotionally intelligent conversations. This model excels in empathetic engagement, offering understanding and support through dialogue spanning various topics and situations. Its goal is to serve as a supportive AI companion, adept at resonating with users' emotions and communication requirements. ## Method We gathered a large volume of high-quality human chat data, which was then filtered and refined to create three types of datasets: 1. DPOย - Initially, we trained the AI on a substantial DPO dataset to grasp human conversation patterns, enabling it to discern which types of output to generate and which to avoid. 2. Alpacaย - Subsequently, we trained it on an Alpaca-type dataset to enhance its human-like responses. 3. SFTย - Finally, once the AI fully comprehended human interactions, we trained it on an SFT dataset to broaden its knowledge base. ## Evaluation ![By/KingNish/Banchmark/png](https://cdn-uploads.huggingface.co/production/uploads/6612aedf09f16e7347dfa7e1/xvS57q5kU9f3AX-O-al0k.png) ## Conclusion: HelpingAI is a large step toward understanding human emoions and give response like them. It can help us lot in making AI better for NLP. Thanks! Model Link: - https://huggingface.co/OEvortex/HelpingAI-9B Demo link: - https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B
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2024-05-29T11:04:31.000Z
2024-06-15T16:59:09.001Z
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/posts/Abhaykoul/167382988688330
2,580
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585651795291491
[ { "type": "text", "value": "Successfully defended my thesis yesterday ๐Ÿš€", "raw": "Successfully defended my thesis yesterday ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Glad that my supervisor gets the innovation behind it - โ€œAn Adaptive Virtual Intelligent Tutor that autonomously learns and adjusts to your learning preferencesโ€", "raw": "Glad that my supervisor gets the innovation behind it - โ€œAn Adaptive Virtual Intelligent Tutor that autonomously learns and adjusts to your learning preferencesโ€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The Intuitive Approach: fine-tune a high performant pre-trained large language model on a rich task-specific dataset (in my case code instruction dataset with adaptive instructions on how to teach coding/solve coding problems with adherence to the studentโ€™s learning style) ", "raw": "The Intuitive Approach: fine-tune a high performant pre-trained large language model on a rich task-specific dataset (in my case code instruction dataset with adaptive instructions on how to teach coding/solve coding problems with adherence to the studentโ€™s learning style) ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Then apply Retrieval-Augmented Generation (RAG) during inference to update the knowledge base of the model in real-time with adaptive features learned from conversations with the model over time.", "raw": "Then apply Retrieval-Augmented Generation (RAG) during inference to update the knowledge base of the model in real-time with adaptive features learned from conversations with the model over time.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The app supports both real-time voice chat with an intelligent 3D Avatar (made with Soulmachine's Digital DNA studio) powered by the fine-tuned model and text chat with the locally hosted fine-tuned model.", "raw": "The app supports both real-time voice chat with an intelligent 3D Avatar (made with Soulmachine's Digital DNA studio) powered by the fine-tuned model and text chat with the locally hosted fine-tuned model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "With enough interactions you get an objective driven, task-specific and adaptive personalized tutor that completely gets you (knows your learning pace, your learning style and preferences).", "raw": "With enough interactions you get an objective driven, task-specific and adaptive personalized tutor that completely gets you (knows your learning pace, your learning style and preferences).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is what I feel is missing in todayโ€™s AI systems - Autonomously Adaptive Assistants (AAA) - and oh Iโ€™m currently writing a paper on this:)", "raw": "This is what I feel is missing in todayโ€™s AI systems - Autonomously Adaptive Assistants (AAA) - and oh Iโ€™m currently writing a paper on this:)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Successfully defended my thesis yesterday ๐Ÿš€ Glad that my supervisor gets the innovation behind it - โ€œAn Adaptive Virtual Intelligent Tutor that autonomously learns and adjusts to your learning preferencesโ€ The Intuitive Approach: fine-tune a high performant pre-trained large language model on a rich task-specific dataset (in my case code instruction dataset with adaptive instructions on how to teach coding/solve coding problems with adherence to the studentโ€™s learning style) Then apply Retrieval-Augmented Generation (RAG) during inference to update the knowledge base of the model in real-time with adaptive features learned from conversations with the model over time. The app supports both real-time voice chat with an intelligent 3D Avatar (made with Soulmachine's Digital DNA studio) powered by the fine-tuned model and text chat with the locally hosted fine-tuned model. With enough interactions you get an objective driven, task-specific and adaptive personalized tutor that completely gets you (knows your learning pace, your learning style and preferences). This is what I feel is missing in todayโ€™s AI systems - Autonomously Adaptive Assistants (AAA) - and oh Iโ€™m currently writing a paper on this:)
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2024-05-29T10:57:39.000Z
2024-05-29T22:29:46.990Z
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/posts/Jaward/585651795291491
1,411
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529180921480217
[ { "type": "text", "value": "I'm working on talking head generation that takes audio and video as input, can someone suggest me a good existing architecture that can generate videos with less latency or can we make it in real time? ", "raw": "I'm working on talking head generation that takes audio and video as input, can someone suggest me a good existing architecture that can generate videos with less latency or can we make it in real time? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I'm working on talking head generation that takes audio and video as input, can someone suggest me a good existing architecture that can generate videos with less latency or can we make it in real time?
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2024-05-29T06:49:21.000Z
2024-05-29T22:33:31.424Z
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/posts/hakunamatata1997/529180921480217
1,519
4
234038368980154
[ { "type": "text", "value": "๐Ÿ’ชBuild an information retrieval Agent that can beat Gemini and OpenAI using open-source Large Action Model framework! ", "raw": "๐Ÿ’ชBuild an information retrieval Agent that can beat Gemini and OpenAI using open-source Large Action Model framework! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In this video, we ask to different proprietary Conversational AI the question:", "raw": "In this video, we ask to different proprietary Conversational AI the question:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " โ€œWhat is the most trendy recent paper on Llava models on Hugging Face papers? Provide the date and a summary of the paperโ€, and the results are interesting!", "raw": " โ€œWhat is the most trendy recent paper on Llava models on Hugging Face papers? Provide the date and a summary of the paperโ€, and the results are interesting!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โŒGemini: found a paper from Jan 29, 2024", "raw": "โŒGemini: found a paper from Jan 29, 2024", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โŒOpenAI: found a paper from October 2023", "raw": "โŒOpenAI: found a paper from October 2023", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โŒYou.com: found a paper from Jan 29 2024", "raw": "โŒYou.com: found a paper from Jan 29 2024", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ…LaVague: found the latest paper (ConvLlaVA which is dope by the way ", "raw": "โœ…LaVague: found the latest paper (ConvLlaVA which is dope by the way ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2405.15738", "href": "https://arxiv.org/abs/2405.15738", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ")! ", "raw": ")! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The best? Our solution fits a few ines of code with our open-source framework! I will share how we built that agent during our webinar on AI Web Agents, this Thursday 30th May at 9 am PST (", "raw": "The best? Our solution fits a few ines of code with our open-source framework! I will share how we built that agent during our webinar on AI Web Agents, this Thursday 30th May at 9 am PST (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://lu.ma/m8fzmb3q", "href": "https://lu.ma/m8fzmb3q", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") so donโ€™t miss it ๐Ÿ˜‰", "raw": ") so donโ€™t miss it ๐Ÿ˜‰", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can also start playing with our framework: ", "raw": "You can also start playing with our framework: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/lavague-ai/LaVague", "href": "https://github.com/lavague-ai/LaVague", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ’ชBuild an information retrieval Agent that can beat Gemini and OpenAI using open-source Large Action Model framework! In this video, we ask to different proprietary Conversational AI the question: โ€œWhat is the most trendy recent paper on Llava models on Hugging Face papers? Provide the date and a summary of the paperโ€, and the results are interesting! โŒGemini: found a paper from Jan 29, 2024 โŒOpenAI: found a paper from October 2023 โŒYou.com: found a paper from Jan 29 2024 โœ…LaVague: found the latest paper (ConvLlaVA which is dope by the way https://arxiv.org/abs/2405.15738)! The best? Our solution fits a few ines of code with our open-source framework! I will share how we built that agent during our webinar on AI Web Agents, this Thursday 30th May at 9 am PST (https://lu.ma/m8fzmb3q) so donโ€™t miss it ๐Ÿ˜‰ You can also start playing with our framework: https://github.com/lavague-ai/LaVague
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2024-05-29T06:36:06.000Z
2024-05-29T06:36:06.227Z
[]
/posts/dhuynh95/234038368980154
1,598
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499465867839623
[ { "type": "text", "value": "Cohere for AI, Argilla, and Hugging Face are collaborating on an Open Science Project to enhance multilingual model evaluations. The project focuses on the widely-used MMLU dataset, which spans 57 subjects like mathematics, computer science, and law. However, existing translations often miss linguistic and cultural nuances, thus embedding biases. ๐Ÿค”", "raw": "Cohere for AI, Argilla, and Hugging Face are collaborating on an Open Science Project to enhance multilingual model evaluations. The project focuses on the widely-used MMLU dataset, which spans 57 subjects like mathematics, computer science, and law. However, existing translations often miss linguistic and cultural nuances, thus embedding biases. ๐Ÿค”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "To address this, they have annotated a subset of the MMLU test set and are inviting global perspectives to review prompts, highlighting cultural specifics and required knowledge. They have mentioned that insights will help shape future multilingual model evaluations, ensuring they are more inclusive and accurate. ๐Ÿ—บ๏ธ ๐Ÿ“ ๐Ÿ™Œ ", "raw": "To address this, they have annotated a subset of the MMLU test set and are inviting global perspectives to review prompts, highlighting cultural specifics and required knowledge. They have mentioned that insights will help shape future multilingual model evaluations, ensuring they are more inclusive and accurate. ๐Ÿ—บ๏ธ ๐Ÿ“ ๐Ÿ™Œ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โ–ถ๏ธ To get started go to: ", "raw": "โ–ถ๏ธ To get started go to: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/CohereForAI/MMLU-evaluation", "href": null, "resource": { "type": "space", "id": "CohereForAI/MMLU-evaluation", "discussionNum": null }, "url": "https://huggingface.co/spaces/CohereForAI/MMLU-evaluation", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŒ They also have an Aya Discord server for collaboration with other participants: ", "raw": "๐ŸŒ They also have an Aya Discord server for collaboration with other participants: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discord.gg/9gVhdfnQMN", "href": "https://discord.gg/9gVhdfnQMN", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Cohere for AI, Argilla, and Hugging Face are collaborating on an Open Science Project to enhance multilingual model evaluations. The project focuses on the widely-used MMLU dataset, which spans 57 subjects like mathematics, computer science, and law. However, existing translations often miss linguistic and cultural nuances, thus embedding biases. ๐Ÿค” To address this, they have annotated a subset of the MMLU test set and are inviting global perspectives to review prompts, highlighting cultural specifics and required knowledge. They have mentioned that insights will help shape future multilingual model evaluations, ensuring they are more inclusive and accurate. ๐Ÿ—บ๏ธ ๐Ÿ“ ๐Ÿ™Œ โ–ถ๏ธ To get started go to: https://huggingface.co/spaces/CohereForAI/MMLU-evaluation ๐ŸŒ They also have an Aya Discord server for collaboration with other participants: https://discord.gg/9gVhdfnQMN
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2024-05-29T06:34:33.000Z
2024-05-29T06:46:05.110Z
[]
/posts/Taylor658/499465867839623
1,244
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727049657779135
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I'm officially considered #gpu_poor ๐Ÿ’€ But I'm #data_rich ๐Ÿ˜Ž
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2024-05-28T23:26:22.000Z
2024-05-28T23:26:22.874Z
[]
/posts/alielfilali01/727049657779135
1,982
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829520382099725
[ { "type": "text", "value": "๐—ฃ๐—ฟ๐—ผ๐˜๐—ผ๐˜๐˜†๐—ฝ๐—ถ๐—ป๐—ด holds an important place in machine learning. But it has traditionally been quite difficult to go from prototype code to production-ready APIs", "raw": "๐—ฃ๐—ฟ๐—ผ๐˜๐—ผ๐˜๐˜†๐—ฝ๐—ถ๐—ป๐—ด holds an important place in machine learning. But it has traditionally been quite difficult to go from prototype code to production-ready APIs", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We're working on making that a lot easier with ๐—š๐—ฟ๐—ฎ๐—ฑ๐—ถ๐—ผ and will unveil something new on June 6th: ", "raw": "We're working on making that a lot easier with ๐—š๐—ฟ๐—ฎ๐—ฑ๐—ถ๐—ผ and will unveil something new on June 6th: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.youtube.com/watch?v=44vi31hehw4&ab_channel=HuggingFace", "href": "https://www.youtube.com/watch?v=44vi31hehw4&ab_channel=HuggingFace", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐—ฃ๐—ฟ๐—ผ๐˜๐—ผ๐˜๐˜†๐—ฝ๐—ถ๐—ป๐—ด holds an important place in machine learning. But it has traditionally been quite difficult to go from prototype code to production-ready APIs We're working on making that a lot easier with ๐—š๐—ฟ๐—ฎ๐—ฑ๐—ถ๐—ผ and will unveil something new on June 6th: https://www.youtube.com/watch?v=44vi31hehw4&ab_channel=HuggingFace
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2024-05-28T22:11:24.000Z
2024-06-11T04:16:32.334Z
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/posts/abidlabs/829520382099725
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897960043054298
[ { "type": "text", "value": "everything-ai v2.0.1: more AI power on your Desktop", "raw": "everything-ai v2.0.1: more AI power on your Desktop", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What is everything-ai?", "raw": "What is everything-ai?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค– everything-ai is natively a multi-tasking agent, 100% local, that is able to perform several AI-related tasks", "raw": "๐Ÿค– everything-ai is natively a multi-tasking agent, 100% local, that is able to perform several AI-related tasks", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " What's new?", "raw": " What's new?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ I am more than thrilled to introduce some new functionalities that were added since last release:", "raw": "๐Ÿš€ I am more than thrilled to introduce some new functionalities that were added since last release:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- ๐ŸŽ™๏ธ๐Ÿ”Š Handle audio files or microphone recordings, classifying or transcribing them with almost every audio-classification and automatic-speech-recognition model on Hugging Face Hub.", "raw": "- ๐ŸŽ™๏ธ๐Ÿ”Š Handle audio files or microphone recordings, classifying or transcribing them with almost every audio-classification and automatic-speech-recognition model on Hugging Face Hub.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- ๐Ÿ“ฝ๏ธ Generate video from text prompts with almost every text-to-video model on HuggingFace Hub (original architecture by [Vasiliy Katsyka](", "raw": "- ๐Ÿ“ฝ๏ธ Generate video from text prompts with almost every text-to-video model on HuggingFace Hub (original architecture by [Vasiliy Katsyka](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/Vasiliy-katsyka)", "href": "https://github.com/Vasiliy-katsyka)", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ")", "raw": ")", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- ๐Ÿงฌ Predict the 3D structure of proteins from their amino-acidic sequence, with EsmFold by AI at Meta ([demo](", "raw": "- ๐Ÿงฌ Predict the 3D structure of proteins from their amino-acidic sequence, with EsmFold by AI at Meta ([demo](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/as-cle-bert/proteinviz)", "href": null, "resource": { "type": "space", "id": "as-cle-bert/proteinviz", "discussionNum": null }, "url": "https://huggingface.co/spaces/as-cle-bert/proteinviz)", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ")", "raw": ")", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- ๐Ÿ‹๏ธ Finetune HF models on several downstream tasks with AutoTrain local integration (AutoTrain is developed by [Abhishek Thakur](", "raw": "- ๐Ÿ‹๏ธ Finetune HF models on several downstream tasks with AutoTrain local integration (AutoTrain is developed by [Abhishek Thakur](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/abhishekkrthakur)", "href": "https://github.com/abhishekkrthakur)", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") ", "raw": ") ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- ๐Ÿ—ฃ๏ธ Unleash powerful LLMs and exploit larger database collections for RAG with the integration of Hugging Face Spaces API and Supabase PostgreSQL databases ([demo](", "raw": "- ๐Ÿ—ฃ๏ธ Unleash powerful LLMs and exploit larger database collections for RAG with the integration of Hugging Face Spaces API and Supabase PostgreSQL databases ([demo](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/as-cle-bert/supabase-ai-chat)", "href": "https://huggingface.co/spaces/as-cle-bert/supabase-ai-chat)", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ")", "raw": ")", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "How can you use all of these features? ", "raw": "How can you use all of these features? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You just need a ", "raw": "You just need a ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`docker compose up`", "href": null, "resource": null, "url": null, "code": "docker compose up", "user": null, "label": null, "lang": null }, { "type": "text", "value": "!๐Ÿ‹", "raw": "!๐Ÿ‹", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Where can I find everything I need?", "raw": "Where can I find everything I need?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Get the source code (and leave a little โญ while you're there):", "raw": "Get the source code (and leave a little โญ while you're there):", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/AstraBert/everything-ai", "href": "https://github.com/AstraBert/everything-ai", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Get a quick-start with the documentation:", "raw": "Get a quick-start with the documentation:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://astrabert.github.io/everything-ai/", "href": "https://astrabert.github.io/everything-ai/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Credits and inspiration", "raw": "Credits and inspiration", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Shout-outs to Hugging Face, Gradio, Docker, AI at Meta, Abhishek Thakur, Qdrant, LangChain and Supabase for making all of this possible!", "raw": "Shout-outs to Hugging Face, Gradio, Docker, AI at Meta, Abhishek Thakur, Qdrant, LangChain and Supabase for making all of this possible!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Inspired by: Jan, Cheshire Cat AI, LM Studio, Ollama and other awesome local AI solutions!", "raw": "Inspired by: Jan, Cheshire Cat AI, LM Studio, Ollama and other awesome local AI solutions!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
everything-ai v2.0.1: more AI power on your Desktop What is everything-ai? ๐Ÿค– everything-ai is natively a multi-tasking agent, 100% local, that is able to perform several AI-related tasks What's new? ๐Ÿš€ I am more than thrilled to introduce some new functionalities that were added since last release: - ๐ŸŽ™๏ธ๐Ÿ”Š Handle audio files or microphone recordings, classifying or transcribing them with almost every audio-classification and automatic-speech-recognition model on Hugging Face Hub. - ๐Ÿ“ฝ๏ธ Generate video from text prompts with almost every text-to-video model on HuggingFace Hub (original architecture by [Vasiliy Katsyka](https://github.com/Vasiliy-katsyka)) - ๐Ÿงฌ Predict the 3D structure of proteins from their amino-acidic sequence, with EsmFold by AI at Meta ([demo](https://huggingface.co/spaces/as-cle-bert/proteinviz)) - ๐Ÿ‹๏ธ Finetune HF models on several downstream tasks with AutoTrain local integration (AutoTrain is developed by [Abhishek Thakur](https://github.com/abhishekkrthakur)) - ๐Ÿ—ฃ๏ธ Unleash powerful LLMs and exploit larger database collections for RAG with the integration of Hugging Face Spaces API and Supabase PostgreSQL databases ([demo](https://huggingface.co/spaces/as-cle-bert/supabase-ai-chat)) How can you use all of these features? You just need a `docker compose up`!๐Ÿ‹ Where can I find everything I need? Get the source code (and leave a little โญ while you're there): https://github.com/AstraBert/everything-ai Get a quick-start with the documentation: https://astrabert.github.io/everything-ai/ Credits and inspiration Shout-outs to Hugging Face, Gradio, Docker, AI at Meta, Abhishek Thakur, Qdrant, LangChain and Supabase for making all of this possible! Inspired by: Jan, Cheshire Cat AI, LM Studio, Ollama and other awesome local AI solutions!
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2024-05-28T20:31:03.000Z
2024-05-28T20:32:30.584Z
[]
/posts/as-cle-bert/897960043054298
1,242
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618994430912738
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Just released ViLaH - a compact 3B parameter vision language model! which generates responses in Hindi only hindi for now ๐Ÿ˜” https://huggingface.co/BhashaAI/ViLaH
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2024-05-28T15:44:55.000Z
2024-05-28T16:14:39.792Z
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/posts/damerajee/618994430912738
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[ { "type": "text", "value": "Weekly highlights for the HF ecosystem!", "raw": "Weekly highlights for the HF ecosystem!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ Phi 3", "raw": "๐Ÿš€ Phi 3", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿฆ… Falcon VLM", "raw": "๐Ÿฆ… Falcon VLM", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค— sentence-transformers v3.0 is here! Train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks and more!", "raw": "๐Ÿค— sentence-transformers v3.0 is here! Train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks and more!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿฅณ Gradio launch event 6/6! We're launching 1.0 versions of two new libraries, Python + JS client libraries to programmatically query Gradio apps, and several new features making it easier to use Gradio apps in production!", "raw": "๐Ÿฅณ Gradio launch event 6/6! We're launching 1.0 versions of two new libraries, Python + JS client libraries to programmatically query Gradio apps, and several new features making it easier to use Gradio apps in production!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœจ Tools now available in HuggingChat! Use any AI apps built by the community! ๐Ÿ”ฅ", "raw": "โœจ Tools now available in HuggingChat! Use any AI apps built by the community! ๐Ÿ”ฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸงŠ ML for 3D Course Unit 3 is here! Covering Gaussian splatting, how it fits in the generative 3D pipeline, and hands-on code to build your own demo!", "raw": "๐ŸงŠ ML for 3D Course Unit 3 is here! Covering Gaussian splatting, how it fits in the generative 3D pipeline, and hands-on code to build your own demo!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "See the full list here!", "raw": "See the full list here!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discord.com/channels/879548962464493619/897387888663232554/1245036889539612764", "href": "https://discord.com/channels/879548962464493619/897387888663232554/1245036889539612764", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " !", "raw": " !", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Weekly highlights for the HF ecosystem! ๐Ÿš€ Phi 3 ๐Ÿฆ… Falcon VLM ๐Ÿค— sentence-transformers v3.0 is here! Train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks and more! ๐Ÿฅณ Gradio launch event 6/6! We're launching 1.0 versions of two new libraries, Python + JS client libraries to programmatically query Gradio apps, and several new features making it easier to use Gradio apps in production! โœจ Tools now available in HuggingChat! Use any AI apps built by the community! ๐Ÿ”ฅ ๐ŸงŠ ML for 3D Course Unit 3 is here! Covering Gaussian splatting, how it fits in the generative 3D pipeline, and hands-on code to build your own demo! See the full list here! https://discord.com/channels/879548962464493619/897387888663232554/1245036889539612764 !
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2024-05-28T15:44:17.000Z
2024-05-29T06:08:05.474Z
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/posts/lunarflu/194020490266627
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[ { "type": "text", "value": "๐Ÿงจ Diffusers 0.28.0 is out ๐Ÿ”ฅ", "raw": "๐Ÿงจ Diffusers 0.28.0 is out ๐Ÿ”ฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It features the first non-generative pipeline of the library -- Marigold ๐Ÿฅ", "raw": "It features the first non-generative pipeline of the library -- Marigold ๐Ÿฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Marigold shines at performing Depth Estimation and Surface Normal Estimation. It was contributed by ", "raw": "Marigold shines at performing Depth Estimation and Surface Normal Estimation. It was contributed by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@toshas", "href": null, "resource": null, "url": null, "code": null, "user": "toshas", "label": null, "lang": null }, { "type": "text", "value": ", one of the authors of Marigold. ", "raw": ", one of the authors of Marigold. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This release also features a massive refactor (led by ", "raw": "This release also features a massive refactor (led by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@DN6", "href": null, "resource": null, "url": null, "code": null, "user": "DN6", "label": null, "lang": null }, { "type": "text", "value": ") of the ", "raw": ") of the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`from_single_file()`", "href": null, "resource": null, "url": null, "code": "from_single_file()", "user": null, "label": null, "lang": null }, { "type": "text", "value": " method, highlighting our efforts for making our library more amenable to community features ๐Ÿค—", "raw": " method, highlighting our efforts for making our library more amenable to community features ๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check out the release notes here:", "raw": "Check out the release notes here:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/huggingface/diffusers/releases/tag/v0.28.0", "href": "https://github.com/huggingface/diffusers/releases/tag/v0.28.0", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿงจ Diffusers 0.28.0 is out ๐Ÿ”ฅ It features the first non-generative pipeline of the library -- Marigold ๐Ÿฅ Marigold shines at performing Depth Estimation and Surface Normal Estimation. It was contributed by @toshas, one of the authors of Marigold. This release also features a massive refactor (led by @DN6) of the `from_single_file()` method, highlighting our efforts for making our library more amenable to community features ๐Ÿค— Check out the release notes here: https://github.com/huggingface/diffusers/releases/tag/v0.28.0
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2024-05-28T14:51:06.000Z
2024-05-28T14:51:38.853Z
[]
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[ { "type": "text", "value": "cooking up something....anyone interested in a daily activity tracker for HF? ", "raw": "cooking up something....anyone interested in a daily activity tracker for HF? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
cooking up something....anyone interested in a daily activity tracker for HF?
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[]
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2024-05-28T14:40:16.000Z
2024-06-03T19:17:07.406Z
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[ { "type": "text", "value": "How can AI help us write better headlines and reach more people?", "raw": "How can AI help us write better headlines and reach more people?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I experimented with a new approach that is both useful and fun. It can help you overcome writerโ€™s block, find better headlines, and make your blog posts and news articles climb in search engine results. Plus, we will learn new concepts along the way!", "raw": "I experimented with a new approach that is both useful and fun. It can help you overcome writerโ€™s block, find better headlines, and make your blog posts and news articles climb in search engine results. Plus, we will learn new concepts along the way!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1๏ธโƒฃ First, I scraped all the blog posts written on Hugging Face to create a dataset with the headlines, texts, dates, and authors' names.", "raw": "1๏ธโƒฃ First, I scraped all the blog posts written on Hugging Face to create a dataset with the headlines, texts, dates, and authors' names.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2๏ธโƒฃ I filtered the dataset to remove posts that were too long and would require a model with a longer context window. This was done to keep the project simple and cost-effective (actually, free).", "raw": "2๏ธโƒฃ I filtered the dataset to remove posts that were too long and would require a model with a longer context window. This was done to keep the project simple and cost-effective (actually, free).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3๏ธโƒฃ Then, I used a dataset generation workflow built by ", "raw": "3๏ธโƒฃ Then, I used a dataset generation workflow built by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@davanstrien", "href": null, "resource": null, "url": null, "code": null, "user": "davanstrien", "label": null, "lang": null }, { "type": "text", "value": " to generate a DPO dataset. ", "raw": " to generate a DPO dataset. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "4๏ธโƒฃ As a last step, you can collectively rate these evaluations to improve the quality of the dataset using an easy-to-use interface with Argilla. Take a look at it and rate some of them! This way, you can contribute to making this dataset useful for different newsrooms that could use it as a starting point.", "raw": "4๏ธโƒฃ As a last step, you can collectively rate these evaluations to improve the quality of the dataset using an easy-to-use interface with Argilla. Take a look at it and rate some of them! This way, you can contribute to making this dataset useful for different newsrooms that could use it as a starting point.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐–๐ก๐ฒ ๐ข๐ญ ๐ฆ๐š๐ญ๐ญ๐ž๐ซ๐ฌ. This example is compelling because, if you look at the dataset, you can see some examples where the headlines are enhanced by the addition of an important keyword or an action verb.", "raw": "๐–๐ก๐ฒ ๐ข๐ญ ๐ฆ๐š๐ญ๐ญ๐ž๐ซ๐ฌ. This example is compelling because, if you look at the dataset, you can see some examples where the headlines are enhanced by the addition of an important keyword or an action verb.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "These tweaks can have a big impact on your position in search engines and, therefore, on your traffic. Itโ€™s also good leverage for our creativity since you can compare the initial idea with another one from an outside perspective.", "raw": "These tweaks can have a big impact on your position in search engines and, therefore, on your traffic. Itโ€™s also good leverage for our creativity since you can compare the initial idea with another one from an outside perspective.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Imagine if youโ€™re a large news organization; you could run this experiment with thousands of news articles.", "raw": "Imagine if youโ€™re a large news organization; you could run this experiment with thousands of news articles.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "With a dataset of several hundred to thousands of entries, you could fine-tune a model to suggest headlines better tailored to your needs and writing style.", "raw": "With a dataset of several hundred to thousands of entries, you could fine-tune a model to suggest headlines better tailored to your needs and writing style.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ Take a look at it and rate the headlines ", "raw": "๐Ÿ‘‰ Take a look at it and rate the headlines ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/fdaudens/journalism-argilla-space", "href": null, "resource": { "type": "space", "id": "fdaudens/journalism-argilla-space", "discussionNum": null }, "url": "https://huggingface.co/spaces/fdaudens/journalism-argilla-space", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ Daniel's code ", "raw": "๐Ÿ‘‰ Daniel's code ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/huggingface/data-is-better-together/blob/main/dpo/README.md", "href": "https://github.com/huggingface/data-is-better-together/blob/main/dpo/README.md", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
How can AI help us write better headlines and reach more people? I experimented with a new approach that is both useful and fun. It can help you overcome writerโ€™s block, find better headlines, and make your blog posts and news articles climb in search engine results. Plus, we will learn new concepts along the way! 1๏ธโƒฃ First, I scraped all the blog posts written on Hugging Face to create a dataset with the headlines, texts, dates, and authors' names. 2๏ธโƒฃ I filtered the dataset to remove posts that were too long and would require a model with a longer context window. This was done to keep the project simple and cost-effective (actually, free). 3๏ธโƒฃ Then, I used a dataset generation workflow built by @davanstrien to generate a DPO dataset. 4๏ธโƒฃ As a last step, you can collectively rate these evaluations to improve the quality of the dataset using an easy-to-use interface with Argilla. Take a look at it and rate some of them! This way, you can contribute to making this dataset useful for different newsrooms that could use it as a starting point. ๐–๐ก๐ฒ ๐ข๐ญ ๐ฆ๐š๐ญ๐ญ๐ž๐ซ๐ฌ. This example is compelling because, if you look at the dataset, you can see some examples where the headlines are enhanced by the addition of an important keyword or an action verb. These tweaks can have a big impact on your position in search engines and, therefore, on your traffic. Itโ€™s also good leverage for our creativity since you can compare the initial idea with another one from an outside perspective. Imagine if youโ€™re a large news organization; you could run this experiment with thousands of news articles. With a dataset of several hundred to thousands of entries, you could fine-tune a model to suggest headlines better tailored to your needs and writing style. ๐Ÿ‘‰ Take a look at it and rate the headlines https://huggingface.co/spaces/fdaudens/journalism-argilla-space ๐Ÿ‘‰ Daniel's code https://github.com/huggingface/data-is-better-together/blob/main/dpo/README.md
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2024-05-28T14:28:04.000Z
2024-05-28T14:35:47.903Z
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/posts/fdaudens/979154040844111
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776570736305681
[ { "type": "text", "value": "Hello HF fans! Anyone is interested to become cofounder in my future company. I'm working on AI products, edge AI, marketing and social Media. Please feel free to contact me.", "raw": "Hello HF fans! Anyone is interested to become cofounder in my future company. I'm working on AI products, edge AI, marketing and social Media. Please feel free to contact me.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hello HF fans! Anyone is interested to become cofounder in my future company. I'm working on AI products, edge AI, marketing and social Media. Please feel free to contact me.
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2024-05-28T14:10:12.000Z
2024-06-07T11:10:11.461Z
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/posts/paulus5/776570736305681
991
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481640523226106
[ { "type": "text", "value": "๐Ÿ™‹๐Ÿปโ€โ™‚๏ธ Hey there folks ,", "raw": "๐Ÿ™‹๐Ÿปโ€โ™‚๏ธ Hey there folks ,", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@tiiuae", "href": null, "resource": null, "url": null, "code": null, "user": "tiiuae", "label": null, "lang": null }, { "type": "text", "value": " released Falcon 11B Vision Model !", "raw": " released Falcon 11B Vision Model !", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿฆ…๐Ÿฆ…๐Ÿ‘€๐Ÿ‘€", "raw": "๐Ÿฆ…๐Ÿฆ…๐Ÿ‘€๐Ÿ‘€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "it's quite good , and you can try it here : ", "raw": "it's quite good , and you can try it here : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/Tonic/Falcon-Vision", "href": "https://huggingface.co/spaces/Tonic/Falcon-Vision", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ™‹๐Ÿปโ€โ™‚๏ธ Hey there folks , @tiiuae released Falcon 11B Vision Model ! ๐Ÿฆ…๐Ÿฆ…๐Ÿ‘€๐Ÿ‘€ it's quite good , and you can try it here : https://huggingface.co/spaces/Tonic/Falcon-Vision
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2024-05-28T13:50:28.000Z
2024-05-28T13:50:28.312Z
[]
/posts/Tonic/481640523226106
1,021
0
613781645506254
[ { "type": "text", "value": "I would pick ", "raw": "I would pick ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@ylecun", "href": null, "resource": null, "url": null, "code": null, "user": "ylecun", "label": null, "lang": null }, { "type": "text", "value": " over ", "raw": " over ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@elonmuskceo", "href": null, "resource": null, "url": null, "code": null, "user": "elonmuskceo", "label": null, "lang": null }, { "type": "text", "value": " every single day of the week. ", "raw": " every single day of the week. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Despite getting much less $$, recognition & visibility than entrepreneurs, the scientists who publish their groundbreaking research openly are the cornerstone of technological progress & massively contribute to making the world a better place!", "raw": "Despite getting much less $$, recognition & visibility than entrepreneurs, the scientists who publish their groundbreaking research openly are the cornerstone of technological progress & massively contribute to making the world a better place!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I would pick @ylecun over @elonmuskceo every single day of the week. Despite getting much less $$, recognition & visibility than entrepreneurs, the scientists who publish their groundbreaking research openly are the cornerstone of technological progress & massively contribute to making the world a better place!
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2024-05-28T13:30:56.000Z
2024-05-28T14:31:23.519Z
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/posts/clem/613781645506254
1,544
1
872659372583163
[ { "type": "text", "value": "โ€ผ๏ธSentence Transformers v3.0 is out! You can now train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also release 50+ datasets to train on. ", "raw": "โ€ผ๏ธSentence Transformers v3.0 is out! You can now train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also release 50+ datasets to train on. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1๏ธโƒฃ Training Refactor", "raw": "1๏ธโƒฃ Training Refactor", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Embedding models can now be trained using an extensive trainer with a lot of powerful features:", "raw": "Embedding models can now be trained using an extensive trainer with a lot of powerful features:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))", "raw": "- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- bf16 training support; loss logging", "raw": "- bf16 training support; loss logging", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Evaluation datasets + evaluation loss", "raw": "- Evaluation datasets + evaluation loss", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Improved callback support + an excellent Weights & Biases integration", "raw": "- Improved callback support + an excellent Weights & Biases integration", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Gradient checkpointing, gradient accumulation", "raw": "- Gradient checkpointing, gradient accumulation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Improved model card generation", "raw": "- Improved model card generation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Resuming from a training checkpoint without performance loss", "raw": "- Resuming from a training checkpoint without performance loss", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Hyperparameter Optimization", "raw": "- Hyperparameter Optimization", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "and much more!", "raw": "and much more!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read my detailed blogpost to learn about the components that make up this new training approach: ", "raw": "Read my detailed blogpost to learn about the components that make up this new training approach: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/train-sentence-transformers", "href": "https://huggingface.co/blog/train-sentence-transformers", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2๏ธโƒฃ Similarity Score", "raw": "2๏ธโƒฃ Similarity Score", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Not sure how to compare embeddings? Don't worry, you can now use ", "raw": "Not sure how to compare embeddings? Don't worry, you can now use ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`model.similarity(embeddings1, embeddings2)`", "href": null, "resource": null, "url": null, "code": "model.similarity(embeddings1, embeddings2)", "user": null, "label": null, "lang": null }, { "type": "text", "value": " and you'll get your similarity scores immediately. Model authors can specify their desired similarity score, so you don't have to worry about it anymore!", "raw": " and you'll get your similarity scores immediately. Model authors can specify their desired similarity score, so you don't have to worry about it anymore!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3๏ธโƒฃ Additional Kwargs", "raw": "3๏ธโƒฃ Additional Kwargs", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Sentence Transformers relies on various Transformers instances (AutoModel, AutoTokenizer, AutoConfig), but it was hard to provide valuable keyword arguments to these (like 'torch_dtype=torch.bfloat16' to load a model a lower precision for 2x inference speedup). This is now easy!", "raw": "Sentence Transformers relies on various Transformers instances (AutoModel, AutoTokenizer, AutoConfig), but it was hard to provide valuable keyword arguments to these (like 'torch_dtype=torch.bfloat16' to load a model a lower precision for 2x inference speedup). This is now easy!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "4๏ธโƒฃ Hyperparameter Optimization", "raw": "4๏ธโƒฃ Hyperparameter Optimization", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task.", "raw": "Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "5๏ธโƒฃ Dataset Release", "raw": "5๏ธโƒฃ Dataset Release", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "To help you out with finetuning models, I've released 50+ ready-to-go datasets that can be used with training or finetuning embedding models: ", "raw": "To help you out with finetuning models, I've released 50+ ready-to-go datasets that can be used with training or finetuning embedding models: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552", "href": null, "resource": { "type": "collection", "id": "sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552", "discussionNum": null }, "url": "https://huggingface.co/collections/sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Full release notes: ", "raw": "Full release notes: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.0", "href": "https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.0", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
โ€ผ๏ธSentence Transformers v3.0 is out! You can now train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also release 50+ datasets to train on. 1๏ธโƒฃ Training Refactor Embedding models can now be trained using an extensive trainer with a lot of powerful features: - MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP)) - bf16 training support; loss logging - Evaluation datasets + evaluation loss - Improved callback support + an excellent Weights & Biases integration - Gradient checkpointing, gradient accumulation - Improved model card generation - Resuming from a training checkpoint without performance loss - Hyperparameter Optimization and much more! Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-sentence-transformers 2๏ธโƒฃ Similarity Score Not sure how to compare embeddings? Don't worry, you can now use `model.similarity(embeddings1, embeddings2)` and you'll get your similarity scores immediately. Model authors can specify their desired similarity score, so you don't have to worry about it anymore! 3๏ธโƒฃ Additional Kwargs Sentence Transformers relies on various Transformers instances (AutoModel, AutoTokenizer, AutoConfig), but it was hard to provide valuable keyword arguments to these (like 'torch_dtype=torch.bfloat16' to load a model a lower precision for 2x inference speedup). This is now easy! 4๏ธโƒฃ Hyperparameter Optimization Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task. 5๏ธโƒฃ Dataset Release To help you out with finetuning models, I've released 50+ ready-to-go datasets that can be used with training or finetuning embedding models: https://huggingface.co/collections/sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552 Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.0
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2024-05-28T12:15:10.000Z
2024-05-28T12:15:10.526Z
[]
/posts/tomaarsen/872659372583163
1,938
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[ { "type": "text", "value": "โœจ Tools are now available in HuggingChat (", "raw": "โœจ Tools are now available in HuggingChat (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://hf.co/chat", "href": "https://hf.co/chat", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ")", "raw": ")", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In short, Tools allow HuggingChat to plug any ZeroGPU Space as a tool HuggingChat can use, offering limitless possibilities.", "raw": "In short, Tools allow HuggingChat to plug any ZeroGPU Space as a tool HuggingChat can use, offering limitless possibilities.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For the release we plugged 6 tools that you can use right now on command-R+, we plan to expand to more models.", "raw": "For the release we plugged 6 tools that you can use right now on command-R+, we plan to expand to more models.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We'll also allow you to add your own tools (any ZeroGPU space is compatible). For more info check out this discussion: ", "raw": "We'll also allow you to add your own tools (any ZeroGPU space is compatible). For more info check out this discussion: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/huggingchat/chat-ui/discussions/470", "href": null, "resource": { "type": "space", "id": "huggingchat/chat-ui", "discussionNum": 470 }, "url": "https://huggingface.co/spaces/huggingchat/chat-ui/discussions/470", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Kudos to ", "raw": "Kudos to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@nsarrazin", "href": null, "resource": null, "url": null, "code": null, "user": "nsarrazin", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Saghen", "href": null, "resource": null, "url": null, "code": null, "user": "Saghen", "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@mishig", "href": null, "resource": null, "url": null, "code": null, "user": "mishig", "label": null, "lang": null }, { "type": "text", "value": " for the release <3", "raw": " for the release <3", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
โœจ Tools are now available in HuggingChat (https://hf.co/chat) In short, Tools allow HuggingChat to plug any ZeroGPU Space as a tool HuggingChat can use, offering limitless possibilities. For the release we plugged 6 tools that you can use right now on command-R+, we plan to expand to more models. We'll also allow you to add your own tools (any ZeroGPU space is compatible). For more info check out this discussion: https://huggingface.co/spaces/huggingchat/chat-ui/discussions/470 Kudos to @nsarrazin @Saghen and @mishig for the release <3
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2024-05-28T10:53:18.000Z
2024-05-29T15:22:39.452Z
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/posts/victor/883387509036134
1,539
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[ { "type": "text", "value": "The Hugging Face Computer Vision community will have the first in a series of online hangouts/study groups this Saturday June 1st at 10:00 am EDT.๐Ÿš€", "raw": "The Hugging Face Computer Vision community will have the first in a series of online hangouts/study groups this Saturday June 1st at 10:00 am EDT.๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Join us on the Hugging Face Discord channel for the Hangout! ", "raw": "Join us on the Hugging Face Discord channel for the Hangout! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discord.gg/hugging-face-879548962464493619?event=1243129304863215656", "href": "https://discord.gg/hugging-face-879548962464493619?event=1243129304863215656", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ๐Ÿค—", "raw": " ๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
The Hugging Face Computer Vision community will have the first in a series of online hangouts/study groups this Saturday June 1st at 10:00 am EDT.๐Ÿš€ Join us on the Hugging Face Discord channel for the Hangout! https://discord.gg/hugging-face-879548962464493619?event=1243129304863215656 ๐Ÿค—
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2024-05-28T06:01:29.000Z
2024-05-28T06:01:29.124Z
[]
/posts/Taylor658/983734451196879
1,309
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432123745536924
[ { "type": "text", "value": "I propose \"merge densification\", a style of merger which attempts to transfer the benefits of a denser model to a base model. The model weight in this case is 0.02, which is atypically small for mergers, but high compared to the learning rate used during training. In this case, the expectation is more creative text-generation. More details below:", "raw": "I propose \"merge densification\", a style of merger which attempts to transfer the benefits of a denser model to a base model. The model weight in this case is 0.02, which is atypically small for mergers, but high compared to the learning rate used during training. In this case, the expectation is more creative text-generation. More details below:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/grimjim/kunoichi-lemon-royale-v3-32K-7B", "href": null, "resource": { "type": "model", "id": "grimjim/kunoichi-lemon-royale-v3-32K-7B", "discussionNum": null }, "url": "https://huggingface.co/grimjim/kunoichi-lemon-royale-v3-32K-7B", "code": null, "user": null, "label": null, "lang": null } ]
I propose "merge densification", a style of merger which attempts to transfer the benefits of a denser model to a base model. The model weight in this case is 0.02, which is atypically small for mergers, but high compared to the learning rate used during training. In this case, the expectation is more creative text-generation. More details below: https://huggingface.co/grimjim/kunoichi-lemon-royale-v3-32K-7B
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2024-05-28T04:09:54.000Z
2024-05-28T18:13:34.200Z
[]
/posts/grimjim/432123745536924
1,684
0
561208015517169
[ { "type": "text", "value": "Remember stacking in ensemble ML? ๐Ÿค”", "raw": "Remember stacking in ensemble ML? ๐Ÿค”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What happens if you do the reverse of that but with LLMs? ๐Ÿคฏ", "raw": "What happens if you do the reverse of that but with LLMs? ๐Ÿคฏ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Basically, MoE created by merging multiple models (instead of being pre-trained like Mixtral)? ๐Ÿง ", "raw": "Basically, MoE created by merging multiple models (instead of being pre-trained like Mixtral)? ๐Ÿง ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Frankenstein MoE! (not an official name) ๐ŸงŸโ€โ™‚๏ธ", "raw": "Frankenstein MoE! (not an official name) ๐ŸงŸโ€โ™‚๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "That's the new Kraken architecture! ๐Ÿ™", "raw": "That's the new Kraken architecture! ๐Ÿ™", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It uses a sequence classification model to route inputs to the most suitable language model based on the input's characteristics. ๐Ÿšฆ", "raw": "It uses a sequence classification model to route inputs to the most suitable language model based on the input's characteristics. ๐Ÿšฆ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Yup, multiple full-fledged LLMs are loaded into memory, and then a classification layer decides who gets to generate an output! ๐ŸŽฐ", "raw": "Yup, multiple full-fledged LLMs are loaded into memory, and then a classification layer decides who gets to generate an output! ๐ŸŽฐ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Tell me you have too many GPUs without telling me you have too many GPUs! ๐Ÿ–ฅ๏ธ๐Ÿ”ฅ", "raw": "Tell me you have too many GPUs without telling me you have too many GPUs! ๐Ÿ–ฅ๏ธ๐Ÿ”ฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Jokes aside, extremely fascinating research but I don't understand why this can't just be a big model with multiple LORA adapters, that can be decided on the fly? ๐Ÿคทโ€โ™‚๏ธ", "raw": "Jokes aside, extremely fascinating research but I don't understand why this can't just be a big model with multiple LORA adapters, that can be decided on the fly? ๐Ÿคทโ€โ™‚๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/cognitivecomputations/Kraken", "href": null, "resource": { "type": "model", "id": "cognitivecomputations/Kraken", "discussionNum": null }, "url": "https://huggingface.co/cognitivecomputations/Kraken", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Github: ", "raw": "Github: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/cognitivecomputations/kraken", "href": "https://github.com/cognitivecomputations/kraken", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Remember stacking in ensemble ML? ๐Ÿค” What happens if you do the reverse of that but with LLMs? ๐Ÿคฏ Basically, MoE created by merging multiple models (instead of being pre-trained like Mixtral)? ๐Ÿง  Frankenstein MoE! (not an official name) ๐ŸงŸโ€โ™‚๏ธ That's the new Kraken architecture! ๐Ÿ™ It uses a sequence classification model to route inputs to the most suitable language model based on the input's characteristics. ๐Ÿšฆ Yup, multiple full-fledged LLMs are loaded into memory, and then a classification layer decides who gets to generate an output! ๐ŸŽฐ Tell me you have too many GPUs without telling me you have too many GPUs! ๐Ÿ–ฅ๏ธ๐Ÿ”ฅ Jokes aside, extremely fascinating research but I don't understand why this can't just be a big model with multiple LORA adapters, that can be decided on the fly? ๐Ÿคทโ€โ™‚๏ธ Model: https://huggingface.co/cognitivecomputations/Kraken Github: https://github.com/cognitivecomputations/kraken
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2024-05-27T20:32:54.000Z
2024-05-29T11:27:15.806Z
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/posts/singhsidhukuldeep/561208015517169
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505150496684783
[ { "type": "text", "value": "If you're part of the Journalists on Hugging Face community, did you know you can receive notifications on ongoing discussions? ", "raw": "If you're part of the Journalists on Hugging Face community, did you know you can receive notifications on ongoing discussions? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- \"Repo discussions\" for repo discussions you're participating in or mentioned in", "raw": "- \"Repo discussions\" for repo discussions you're participating in or mentioned in", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- \"New activity on watched orgs/users\" for repo discussions & posts from users & orgs", "raw": "- \"New activity on watched orgs/users\" for repo discussions & posts from users & orgs", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "you're watching", "raw": "you're watching", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Activate them here: ", "raw": "Activate them here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/settings/notifications", "href": "https://huggingface.co/settings/notifications", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Join the community: If youโ€™re part of the Journalists on Hugging Face community, did you know you can receive notifications about ongoing discussions?", "raw": "Join the community: If youโ€™re part of the Journalists on Hugging Face community, did you know you can receive notifications about ongoing discussions?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
If you're part of the Journalists on Hugging Face community, did you know you can receive notifications on ongoing discussions? - "Repo discussions" for repo discussions you're participating in or mentioned in - "New activity on watched orgs/users" for repo discussions & posts from users & orgs you're watching Activate them here: https://huggingface.co/settings/notifications Join the community: If youโ€™re part of the Journalists on Hugging Face community, did you know you can receive notifications about ongoing discussions?
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2024-05-27T18:43:30.000Z
2024-05-27T18:43:30.838Z
[]
/posts/fdaudens/505150496684783
965
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607293668883891
[ { "type": "text", "value": "I've just open sourced RAGoon, a small utility I use to integrate knowledge from the web into LLM inference based on Groq speed and pure Google search performance โšก", "raw": "I've just open sourced RAGoon, a small utility I use to integrate knowledge from the web into LLM inference based on Groq speed and pure Google search performance โšก", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "RAGoon is a Python library available on PyPI that aims to improve the performance of language models by providing contextually relevant information through retrieval-based querying, parallel web scraping, and data augmentation techniques. It offers an integration of various APIs (OpenAI, Groq), enabling users to retrieve information from the web, enrich it with domain-specific knowledge, and feed it to language models for more informed responses.", "raw": "RAGoon is a Python library available on PyPI that aims to improve the performance of language models by providing contextually relevant information through retrieval-based querying, parallel web scraping, and data augmentation techniques. It offers an integration of various APIs (OpenAI, Groq), enabling users to retrieve information from the web, enrich it with domain-specific knowledge, and feed it to language models for more informed responses.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```python\nfrom groq import Groq\n# from openai import OpenAI\nfrom ragoon import RAGoon\n\n# Initialize RAGoon instance\nragoon = RAGoon(\n google_api_key=\"your_google_api_key\",\n google_cx=\"your_google_cx\",\n completion_client=Groq(api_key=\"your_groq_api_key\")\n)\n\n# Search and get results\nquery = \"I want to do a left join in python polars\"\nresults = ragoon.search(\n query=query,\n completion_model=\"Llama3-70b-8192\",\n)\n\n# Print list of results\nprint(results)\n```", "href": null, "resource": null, "url": null, "code": "from groq import Groq\n# from openai import OpenAI\nfrom ragoon import RAGoon\n\n# Initialize RAGoon instance\nragoon = RAGoon(\n google_api_key=\"your_google_api_key\",\n google_cx=\"your_google_cx\",\n completion_client=Groq(api_key=\"your_groq_api_key\")\n)\n\n# Search and get results\nquery = \"I want to do a left join in python polars\"\nresults = ragoon.search(\n query=query,\n completion_model=\"Llama3-70b-8192\",\n)\n\n# Print list of results\nprint(results)", "user": null, "label": null, "lang": "python" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For the time being, this project remains simple, but can easily be integrated into a RAG pipeline.", "raw": "For the time being, this project remains simple, but can easily be integrated into a RAG pipeline.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Link to GitHub : ", "raw": "Link to GitHub : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/louisbrulenaudet/ragoon", "href": "https://github.com/louisbrulenaudet/ragoon", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I've just open sourced RAGoon, a small utility I use to integrate knowledge from the web into LLM inference based on Groq speed and pure Google search performance โšก RAGoon is a Python library available on PyPI that aims to improve the performance of language models by providing contextually relevant information through retrieval-based querying, parallel web scraping, and data augmentation techniques. It offers an integration of various APIs (OpenAI, Groq), enabling users to retrieve information from the web, enrich it with domain-specific knowledge, and feed it to language models for more informed responses. ```python from groq import Groq # from openai import OpenAI from ragoon import RAGoon # Initialize RAGoon instance ragoon = RAGoon( google_api_key="your_google_api_key", google_cx="your_google_cx", completion_client=Groq(api_key="your_groq_api_key") ) # Search and get results query = "I want to do a left join in python polars" results = ragoon.search( query=query, completion_model="Llama3-70b-8192", ) # Print list of results print(results) ``` For the time being, this project remains simple, but can easily be integrated into a RAG pipeline. Link to GitHub : https://github.com/louisbrulenaudet/ragoon
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2024-05-27T18:33:23.000Z
2024-05-27T18:33:23.872Z
[]
/posts/louisbrulenaudet/607293668883891
981
0
378045093988402
[ { "type": "text", "value": "๐Ÿš€๐ŸŽญ๐ŸŒŸ New Research Alert - InstructAvatar (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿš€", "raw": "๐Ÿš€๐ŸŽญ๐ŸŒŸ New Research Alert - InstructAvatar (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“„ Title: InstructAvatar: Text-Guided Emotion and Motion Control for Avatar Generation ๐Ÿ”", "raw": "๐Ÿ“„ Title: InstructAvatar: Text-Guided Emotion and Motion Control for Avatar Generation ๐Ÿ”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“ Description: InstructAvatar is a novel method for generating emotionally expressive 2D avatars using text-guided instructions, offering improved emotion control, lip-sync quality, and naturalness. It uses a two-branch diffusion-based generator to predict avatars based on both audio and text input.", "raw": "๐Ÿ“ Description: InstructAvatar is a novel method for generating emotionally expressive 2D avatars using text-guided instructions, offering improved emotion control, lip-sync quality, and naturalness. It uses a two-branch diffusion-based generator to predict avatars based on both audio and text input.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘ฅ Authors: Yuchi Wang et al.", "raw": "๐Ÿ‘ฅ Authors: Yuchi Wang et al.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“„ Paper: ", "raw": "๐Ÿ“„ Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2405.15758", "href": null, "resource": { "type": "paper", "id": "2405.15758", "discussionNum": null }, "url": "https://huggingface.co/papers/2405.15758", "code": null, "user": null, "label": "InstructAvatar: Text-Guided Emotion and Motion Control for Avatar\n Generation (2405.15758)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŒ Github Page: ", "raw": "๐ŸŒ Github Page: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://wangyuchi369.github.io/InstructAvatar/", "href": "https://wangyuchi369.github.io/InstructAvatar/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“ Repository: ", "raw": "๐Ÿ“ Repository: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/wangyuchi369/InstructAvatar", "href": "https://github.com/wangyuchi369/InstructAvatar", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "raw": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "href": null, "resource": { "type": "space", "id": "DmitryRyumin/NewEraAI-Papers", "discussionNum": null }, "url": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " curated by ", "raw": " curated by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@DmitryRyumin", "href": null, "resource": null, "url": null, "code": null, "user": "DmitryRyumin", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ Added to the Avatars Collection: ", "raw": "๐Ÿš€ Added to the Avatars Collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "href": null, "resource": { "type": "collection", "id": "DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "discussionNum": null }, "url": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ” Keywords: #InstructAvatar #AvatarGeneration #EmotionControl #FacialMotion #LipSynchronization #NaturalLanguageInterface #DiffusionBasedGenerator #TextGuidedInstructions #2DAvatars #VideoSynthesis #Interactivity #ComputerGraphics #DeepLearning #ComputerVision #Innovation", "raw": "๐Ÿ” Keywords: #InstructAvatar #AvatarGeneration #EmotionControl #FacialMotion #LipSynchronization #NaturalLanguageInterface #DiffusionBasedGenerator #TextGuidedInstructions #2DAvatars #VideoSynthesis #Interactivity #ComputerGraphics #DeepLearning #ComputerVision #Innovation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿš€๐ŸŽญ๐ŸŒŸ New Research Alert - InstructAvatar (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿš€ ๐Ÿ“„ Title: InstructAvatar: Text-Guided Emotion and Motion Control for Avatar Generation ๐Ÿ” ๐Ÿ“ Description: InstructAvatar is a novel method for generating emotionally expressive 2D avatars using text-guided instructions, offering improved emotion control, lip-sync quality, and naturalness. It uses a two-branch diffusion-based generator to predict avatars based on both audio and text input. ๐Ÿ‘ฅ Authors: Yuchi Wang et al. ๐Ÿ“„ Paper: https://huggingface.co/papers/2405.15758 ๐ŸŒ Github Page: https://wangyuchi369.github.io/InstructAvatar/ ๐Ÿ“ Repository: https://github.com/wangyuchi369/InstructAvatar ๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin ๐Ÿš€ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36 ๐Ÿ” Keywords: #InstructAvatar #AvatarGeneration #EmotionControl #FacialMotion #LipSynchronization #NaturalLanguageInterface #DiffusionBasedGenerator #TextGuidedInstructions #2DAvatars #VideoSynthesis #Interactivity #ComputerGraphics #DeepLearning #ComputerVision #Innovation
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2024-05-27T16:59:22.000Z
2024-05-27T16:59:22.330Z
[]
/posts/DmitryRyumin/378045093988402
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Celebrating 30 likes!! https://huggingface.co/spaces/nroggendorff/epicrealismxl
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2024-05-27T16:42:52.000Z
2024-05-27T16:43:31.150Z
[]
/posts/nroggendorff/768982923455327
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[ { "type": "text", "value": "๐™’๐™ง๐™ž๐™ฉ๐™ž๐™ฃ๐™œ ๐™ฉ๐™ค๐™ค๐™ก ๐™˜๐™–๐™ก๐™ก๐™จ ๐™ž๐™ฃ ๐™˜๐™ค๐™™๐™š ๐™Ÿ๐™ช๐™จ๐™ฉ ๐™ฌ๐™ค๐™ง๐™ ๐™จ ๐™—๐™š๐™ฉ๐™ฉ๐™š๐™ง ๐™ฉ๐™๐™–๐™ฃ ๐™…๐™Ž๐™Š๐™‰ ๐Ÿ’ช", "raw": "๐™’๐™ง๐™ž๐™ฉ๐™ž๐™ฃ๐™œ ๐™ฉ๐™ค๐™ค๐™ก ๐™˜๐™–๐™ก๐™ก๐™จ ๐™ž๐™ฃ ๐™˜๐™ค๐™™๐™š ๐™Ÿ๐™ช๐™จ๐™ฉ ๐™ฌ๐™ค๐™ง๐™ ๐™จ ๐™—๐™š๐™ฉ๐™ฉ๐™š๐™ง ๐™ฉ๐™๐™–๐™ฃ ๐™…๐™Ž๐™Š๐™‰ ๐Ÿ’ช", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I was really happy to learn today by ", "raw": "I was really happy to learn today by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@sergeipetrov", "href": null, "resource": null, "url": null, "code": null, "user": "sergeipetrov", "label": null, "lang": null }, { "type": "text", "value": " that paper ๐˜Œ๐˜น๐˜ฆ๐˜ค๐˜ถ๐˜ต๐˜ข๐˜ฃ๐˜ญ๐˜ฆ ๐˜Š๐˜ฐ๐˜ฅ๐˜ฆ ๐˜ˆ๐˜ค๐˜ต๐˜ช๐˜ฐ๐˜ฏ๐˜ด ๐˜Œ๐˜ญ๐˜ช๐˜ค๐˜ช๐˜ต ๐˜‰๐˜ฆ๐˜ต๐˜ต๐˜ฆ๐˜ณ ๐˜“๐˜“๐˜” ๐˜ˆ๐˜จ๐˜ฆ๐˜ฏ๐˜ต๐˜ด was accepted at ICLR 2024! ", "raw": " that paper ๐˜Œ๐˜น๐˜ฆ๐˜ค๐˜ถ๐˜ต๐˜ข๐˜ฃ๐˜ญ๐˜ฆ ๐˜Š๐˜ฐ๐˜ฅ๐˜ฆ ๐˜ˆ๐˜ค๐˜ต๐˜ช๐˜ฐ๐˜ฏ๐˜ด ๐˜Œ๐˜ญ๐˜ช๐˜ค๐˜ช๐˜ต ๐˜‰๐˜ฆ๐˜ต๐˜ต๐˜ฆ๐˜ณ ๐˜“๐˜“๐˜” ๐˜ˆ๐˜จ๐˜ฆ๐˜ฏ๐˜ต๐˜ด was accepted at ICLR 2024! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "As a reminder, an agent is a system in which you embed a LLM engine, to let it call tools.", "raw": "As a reminder, an agent is a system in which you embed a LLM engine, to let it call tools.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "These tools are meant like an IronMan suit, to supplement the LLM in areas that it isn't good at.", "raw": "These tools are meant like an IronMan suit, to supplement the LLM in areas that it isn't good at.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿง‘โ€๐Ÿ’ป For instance your friendly LLM may be terrible at calculating powers of floating numbers (\"What is X ^0.2947 ?\"), so it should use a calculator.", "raw": "๐Ÿง‘โ€๐Ÿ’ป For instance your friendly LLM may be terrible at calculating powers of floating numbers (\"What is X ^0.2947 ?\"), so it should use a calculator.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”ŽIt may be terrible at knowing precise facts (\"What was the date of the Golden Bull?\") so it should use a web browser.", "raw": "๐Ÿ”ŽIt may be terrible at knowing precise facts (\"What was the date of the Golden Bull?\") so it should use a web browser.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "So the agent system will prompt an agent with \"Now you can use these tools: calculator, search,...\"", "raw": "So the agent system will prompt an agent with \"Now you can use these tools: calculator, search,...\"", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "But ๐™๐™ค๐™ฌ ๐™จ๐™๐™ค๐™ช๐™ก๐™™ ๐™ฉ๐™๐™š ๐™–๐™œ๐™š๐™ฃ๐™ฉ ๐™š๐™ญ๐™ฅ๐™ง๐™š๐™จ๐™จ ๐™ž๐™ฉ๐™จ ๐™–๐™˜๐™ฉ๐™ž๐™ค๐™ฃ๐™จ?", "raw": "But ๐™๐™ค๐™ฌ ๐™จ๐™๐™ค๐™ช๐™ก๐™™ ๐™ฉ๐™๐™š ๐™–๐™œ๐™š๐™ฃ๐™ฉ ๐™š๐™ญ๐™ฅ๐™ง๐™š๐™จ๐™จ ๐™ž๐™ฉ๐™จ ๐™–๐™˜๐™ฉ๐™ž๐™ค๐™ฃ๐™จ?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "All well known frameworks let agents write their actions as JSON strings.", "raw": "All well known frameworks let agents write their actions as JSON strings.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We ๐—ฝ๐—ฟ๐—ฒ๐—ณ๐—ฒ๐—ฟ๐—ฟ๐—ฒ๐—ฑ ๐˜๐—ผ ๐—ด๐—ผ ๐˜„๐—ถ๐˜๐—ต ๐—ณ๐—ผ๐—ฟ๐—บ๐˜‚๐—น๐—ฎ๐˜๐—ถ๐—ป๐—ด ๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—ถ๐—ป ๐—–๐—ผ๐—ฑ๐—ฒ, ๐˜„๐—ต๐—ถ๐—ฐ๐—ต ๐—ถ๐˜€ ๐—บ๐˜‚๐—ฐ๐—ต ๐—บ๐—ผ๐—ฟ๐—ฒ ๐˜ƒ๐—ฒ๐—ฟ๐˜€๐—ฎ๐˜๐—ถ๐—น๐—ฒ ๐—ฎ๐—ป๐—ฑ ๐—ฐ๐—ผ๐—ป๐—ฐ๐—ถ๐˜€๐—ฒ, ๐—ฎ๐—ป๐—ฑ ๐—ฎ๐—น๐—น๐—ผ๐˜„๐˜€ ๐˜๐—ผ ๐—ฐ๐—ต๐—ฎ๐—ถ๐—ป ๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐˜€๐—ฒ๐—ฎ๐—บ๐—น๐—ฒ๐˜€๐˜€๐—น๐˜†: see the picture attached for an example where Code formulation really shines.", "raw": "We ๐—ฝ๐—ฟ๐—ฒ๐—ณ๐—ฒ๐—ฟ๐—ฟ๐—ฒ๐—ฑ ๐˜๐—ผ ๐—ด๐—ผ ๐˜„๐—ถ๐˜๐—ต ๐—ณ๐—ผ๐—ฟ๐—บ๐˜‚๐—น๐—ฎ๐˜๐—ถ๐—ป๐—ด ๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—ถ๐—ป ๐—–๐—ผ๐—ฑ๐—ฒ, ๐˜„๐—ต๐—ถ๐—ฐ๐—ต ๐—ถ๐˜€ ๐—บ๐˜‚๐—ฐ๐—ต ๐—บ๐—ผ๐—ฟ๐—ฒ ๐˜ƒ๐—ฒ๐—ฟ๐˜€๐—ฎ๐˜๐—ถ๐—น๐—ฒ ๐—ฎ๐—ป๐—ฑ ๐—ฐ๐—ผ๐—ป๐—ฐ๐—ถ๐˜€๐—ฒ, ๐—ฎ๐—ป๐—ฑ ๐—ฎ๐—น๐—น๐—ผ๐˜„๐˜€ ๐˜๐—ผ ๐—ฐ๐—ต๐—ฎ๐—ถ๐—ป ๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐˜€๐—ฒ๐—ฎ๐—บ๐—น๐—ฒ๐˜€๐˜€๐—น๐˜†: see the picture attached for an example where Code formulation really shines.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And the paper confirms our choice: researchers show that ๐—ฐ๐—ผ๐—บ๐—ฝ๐—ฎ๐—ฟ๐—ฒ๐—ฑ ๐˜๐—ผ ๐—๐—ฆ๐—ข๐—ก ๐—ผ๐—ฟ ๐—ฝ๐—น๐—ฎ๐—ถ๐—ป ๐˜๐—ฒ๐˜…๐˜, ๐—–๐—ผ๐—ฑ๐—ฒ ๐—ถ๐˜€ ๐—ฏ๐—ฒ๐˜๐˜๐—ฒ๐—ฟ ๐—ฏ๐—ผ๐˜๐—ต ๐—ถ๐—ป ๐—ฐ๐—ผ๐—ป๐—ฐ๐—ถ๐˜€๐—ฒ๐—ป๐—ฒ๐˜€๐˜€ ๐—ฎ๐—ป๐—ฑ ๐—ฝ๐—ฒ๐—ฟ๐—ณ๐—ผ๐—ฟ๐—บ๐—ฎ๐—ป๐—ฐ๐—ฒ:", "raw": "And the paper confirms our choice: researchers show that ๐—ฐ๐—ผ๐—บ๐—ฝ๐—ฎ๐—ฟ๐—ฒ๐—ฑ ๐˜๐—ผ ๐—๐—ฆ๐—ข๐—ก ๐—ผ๐—ฟ ๐—ฝ๐—น๐—ฎ๐—ถ๐—ป ๐˜๐—ฒ๐˜…๐˜, ๐—–๐—ผ๐—ฑ๐—ฒ ๐—ถ๐˜€ ๐—ฏ๐—ฒ๐˜๐˜๐—ฒ๐—ฟ ๐—ฏ๐—ผ๐˜๐—ต ๐—ถ๐—ป ๐—ฐ๐—ผ๐—ป๐—ฐ๐—ถ๐˜€๐—ฒ๐—ป๐—ฒ๐˜€๐˜€ ๐—ฎ๐—ป๐—ฑ ๐—ฝ๐—ฒ๐—ฟ๐—ณ๐—ผ๐—ฟ๐—บ๐—ฎ๐—ป๐—ฐ๐—ฒ:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžค Up to 30% fewer steps for the same actions (much more concise)", "raw": "โžค Up to 30% fewer steps for the same actions (much more concise)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžค Up to 20% higher performance on benchmarks", "raw": "โžค Up to 20% higher performance on benchmarks", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And we find additional benefits, for instance a natural handling of variables.", "raw": "And we find additional benefits, for instance a natural handling of variables.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read the paper here ๐Ÿ“– ", "raw": "Read the paper here ๐Ÿ“– ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2402.01030", "href": null, "resource": { "type": "paper", "id": "2402.01030", "discussionNum": null }, "url": "https://huggingface.co/papers/2402.01030", "code": null, "user": null, "label": "Executable Code Actions Elicit Better LLM Agents (2402.01030)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Get your ReactCodeAgent running with our Agents framework! ๐Ÿ‘‰ ", "raw": "Get your ReactCodeAgent running with our Agents framework! ๐Ÿ‘‰ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/learn/cookbook/agents", "href": "https://huggingface.co/learn/cookbook/agents", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐™’๐™ง๐™ž๐™ฉ๐™ž๐™ฃ๐™œ ๐™ฉ๐™ค๐™ค๐™ก ๐™˜๐™–๐™ก๐™ก๐™จ ๐™ž๐™ฃ ๐™˜๐™ค๐™™๐™š ๐™Ÿ๐™ช๐™จ๐™ฉ ๐™ฌ๐™ค๐™ง๐™ ๐™จ ๐™—๐™š๐™ฉ๐™ฉ๐™š๐™ง ๐™ฉ๐™๐™–๐™ฃ ๐™…๐™Ž๐™Š๐™‰ ๐Ÿ’ช I was really happy to learn today by @sergeipetrov that paper ๐˜Œ๐˜น๐˜ฆ๐˜ค๐˜ถ๐˜ต๐˜ข๐˜ฃ๐˜ญ๐˜ฆ ๐˜Š๐˜ฐ๐˜ฅ๐˜ฆ ๐˜ˆ๐˜ค๐˜ต๐˜ช๐˜ฐ๐˜ฏ๐˜ด ๐˜Œ๐˜ญ๐˜ช๐˜ค๐˜ช๐˜ต ๐˜‰๐˜ฆ๐˜ต๐˜ต๐˜ฆ๐˜ณ ๐˜“๐˜“๐˜” ๐˜ˆ๐˜จ๐˜ฆ๐˜ฏ๐˜ต๐˜ด was accepted at ICLR 2024! As a reminder, an agent is a system in which you embed a LLM engine, to let it call tools. These tools are meant like an IronMan suit, to supplement the LLM in areas that it isn't good at. ๐Ÿง‘โ€๐Ÿ’ป For instance your friendly LLM may be terrible at calculating powers of floating numbers ("What is X ^0.2947 ?"), so it should use a calculator. ๐Ÿ”ŽIt may be terrible at knowing precise facts ("What was the date of the Golden Bull?") so it should use a web browser. So the agent system will prompt an agent with "Now you can use these tools: calculator, search,..." But ๐™๐™ค๐™ฌ ๐™จ๐™๐™ค๐™ช๐™ก๐™™ ๐™ฉ๐™๐™š ๐™–๐™œ๐™š๐™ฃ๐™ฉ ๐™š๐™ญ๐™ฅ๐™ง๐™š๐™จ๐™จ ๐™ž๐™ฉ๐™จ ๐™–๐™˜๐™ฉ๐™ž๐™ค๐™ฃ๐™จ? All well known frameworks let agents write their actions as JSON strings. We ๐—ฝ๐—ฟ๐—ฒ๐—ณ๐—ฒ๐—ฟ๐—ฟ๐—ฒ๐—ฑ ๐˜๐—ผ ๐—ด๐—ผ ๐˜„๐—ถ๐˜๐—ต ๐—ณ๐—ผ๐—ฟ๐—บ๐˜‚๐—น๐—ฎ๐˜๐—ถ๐—ป๐—ด ๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—ถ๐—ป ๐—–๐—ผ๐—ฑ๐—ฒ, ๐˜„๐—ต๐—ถ๐—ฐ๐—ต ๐—ถ๐˜€ ๐—บ๐˜‚๐—ฐ๐—ต ๐—บ๐—ผ๐—ฟ๐—ฒ ๐˜ƒ๐—ฒ๐—ฟ๐˜€๐—ฎ๐˜๐—ถ๐—น๐—ฒ ๐—ฎ๐—ป๐—ฑ ๐—ฐ๐—ผ๐—ป๐—ฐ๐—ถ๐˜€๐—ฒ, ๐—ฎ๐—ป๐—ฑ ๐—ฎ๐—น๐—น๐—ผ๐˜„๐˜€ ๐˜๐—ผ ๐—ฐ๐—ต๐—ฎ๐—ถ๐—ป ๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐˜€๐—ฒ๐—ฎ๐—บ๐—น๐—ฒ๐˜€๐˜€๐—น๐˜†: see the picture attached for an example where Code formulation really shines. And the paper confirms our choice: researchers show that ๐—ฐ๐—ผ๐—บ๐—ฝ๐—ฎ๐—ฟ๐—ฒ๐—ฑ ๐˜๐—ผ ๐—๐—ฆ๐—ข๐—ก ๐—ผ๐—ฟ ๐—ฝ๐—น๐—ฎ๐—ถ๐—ป ๐˜๐—ฒ๐˜…๐˜, ๐—–๐—ผ๐—ฑ๐—ฒ ๐—ถ๐˜€ ๐—ฏ๐—ฒ๐˜๐˜๐—ฒ๐—ฟ ๐—ฏ๐—ผ๐˜๐—ต ๐—ถ๐—ป ๐—ฐ๐—ผ๐—ป๐—ฐ๐—ถ๐˜€๐—ฒ๐—ป๐—ฒ๐˜€๐˜€ ๐—ฎ๐—ป๐—ฑ ๐—ฝ๐—ฒ๐—ฟ๐—ณ๐—ผ๐—ฟ๐—บ๐—ฎ๐—ป๐—ฐ๐—ฒ: โžค Up to 30% fewer steps for the same actions (much more concise) โžค Up to 20% higher performance on benchmarks And we find additional benefits, for instance a natural handling of variables. Read the paper here ๐Ÿ“– https://huggingface.co/papers/2402.01030 Get your ReactCodeAgent running with our Agents framework! ๐Ÿ‘‰ https://huggingface.co/learn/cookbook/agents
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[]
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[ { "reaction": "๐Ÿ”ฅ", "users": [ "Alexandro14", "clem", "Rybens" ], "count": 3 } ]
2024-05-27T16:41:17.000Z
2024-05-27T16:41:17.317Z
[]
/posts/m-ric/822144949711868
836
0
733481799754673
[ { "type": "text", "value": "We will be providing ZeroGPU grants (for Spaces inference) to those who want to fine-tune PaliGemma and build a Space ๐Ÿ”ฅ", "raw": "We will be providing ZeroGPU grants (for Spaces inference) to those who want to fine-tune PaliGemma and build a Space ๐Ÿ”ฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can pick any dataset of your choice!", "raw": "You can pick any dataset of your choice!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Example code: ", "raw": "Example code: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/1x_OEphRK0H97DqqxEyiMewqsTiLD_Xmi?usp=sharing", "href": "https://colab.research.google.com/drive/1x_OEphRK0H97DqqxEyiMewqsTiLD_Xmi?usp=sharing", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " (you can use a lower GPU with QLoRA)", "raw": " (you can use a lower GPU with QLoRA)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Datasets: ", "raw": "Datasets: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=trending", "href": "https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=trending", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/datasets?task_categories=task_categories:image-to-text&sort=trending", "href": "https://huggingface.co/datasets?task_categories=task_categories:image-to-text&sort=trending", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
We will be providing ZeroGPU grants (for Spaces inference) to those who want to fine-tune PaliGemma and build a Space ๐Ÿ”ฅ You can pick any dataset of your choice! Example code: https://colab.research.google.com/drive/1x_OEphRK0H97DqqxEyiMewqsTiLD_Xmi?usp=sharing (you can use a lower GPU with QLoRA) Datasets: https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=trending https://huggingface.co/datasets?task_categories=task_categories:image-to-text&sort=trending
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2024-05-27T12:11:46.000Z
2024-05-29T06:28:13.729Z
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/posts/merve/733481799754673
1,270
5
365623455051476
[ { "type": "text", "value": "๐Ÿ‡ซ๐Ÿ‡ท ", "raw": "๐Ÿ‡ซ๐Ÿ‡ท ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Quel impact de lโ€™IA sur les filiรจres du cinรฉma, de lโ€™audiovisuel et du jeu vidรฉo? ", "raw": "Quel impact de lโ€™IA sur les filiรจres du cinรฉma, de lโ€™audiovisuel et du jeu vidรฉo? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Etude prospective ร  destination des professionnels ", "raw": "Etude prospective ร  destination des professionnels ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โ€” CNC & BearingPoint | 09/04/2024", "raw": "โ€” CNC & BearingPoint | 09/04/2024", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Si lโ€™Intelligence Artificielle (IA) est utilisรฉe de longue date dans les secteurs du cinรฉma, de lโ€™audiovisuel et du jeu vidรฉo, les nouvelles applications de lโ€™IA gรฉnรฉrative bousculent notre vision de ce dont est capable une machine et possรจdent un potentiel de transformation inรฉdit. Elles impressionnent par la qualitรฉ de leurs productions et suscitent par consรฉquent de nombreux dรฉbats, entre attentes et apprรฉhensions.", "raw": "Si lโ€™Intelligence Artificielle (IA) est utilisรฉe de longue date dans les secteurs du cinรฉma, de lโ€™audiovisuel et du jeu vidรฉo, les nouvelles applications de lโ€™IA gรฉnรฉrative bousculent notre vision de ce dont est capable une machine et possรจdent un potentiel de transformation inรฉdit. Elles impressionnent par la qualitรฉ de leurs productions et suscitent par consรฉquent de nombreux dรฉbats, entre attentes et apprรฉhensions.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Le CNC a donc dรฉcider de lancer un nouvel Observatoire de lโ€™IA Afin de mieux comprendre les usages de lโ€™IA et ses impacts rรฉels sur la filiรจre de lโ€™image. Dans le cadre de cet Observatoire, le CNC a souhaitรฉ dresser un premier รฉtat des lieux ร  travers la cartographie des usages actuels ou potentiels de lโ€™IA ร  chaque รฉtape du processus de crรฉation et de diffusion dโ€™une ล“uvre, en identifiant les opportunitรฉs et risques associรฉs, notamment en termes de mรฉtiers et dโ€™emploi. Cette รฉtude CNC / Bearing Point en a prรฉsentรฉ les principaux enseignements le 6 mars, lors de la journรฉe CNC ยซ Crรฉer, produire, diffuser ร  lโ€™heure de lโ€™intelligence artificielle ยป.", "raw": "Le CNC a donc dรฉcider de lancer un nouvel Observatoire de lโ€™IA Afin de mieux comprendre les usages de lโ€™IA et ses impacts rรฉels sur la filiรจre de lโ€™image. Dans le cadre de cet Observatoire, le CNC a souhaitรฉ dresser un premier รฉtat des lieux ร  travers la cartographie des usages actuels ou potentiels de lโ€™IA ร  chaque รฉtape du processus de crรฉation et de diffusion dโ€™une ล“uvre, en identifiant les opportunitรฉs et risques associรฉs, notamment en termes de mรฉtiers et dโ€™emploi. Cette รฉtude CNC / Bearing Point en a prรฉsentรฉ les principaux enseignements le 6 mars, lors de la journรฉe CNC ยซ Crรฉer, produire, diffuser ร  lโ€™heure de lโ€™intelligence artificielle ยป.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Le CNC publie la version augmentรฉe de la cartographie des usages de lโ€™IA dans les filiรจres du cinรฉma, de lโ€™audiovisuel et du jeu vidรฉo.", "raw": "Le CNC publie la version augmentรฉe de la cartographie des usages de lโ€™IA dans les filiรจres du cinรฉma, de lโ€™audiovisuel et du jeu vidรฉo.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Lien vers la cartographie complรจte: ", "raw": "Lien vers la cartographie complรจte: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.cnc.fr/documents/36995/2097582/Cartographie+des+usages+IA_rapport+complet.pdf/96532829-747e-b85e-c74b-af313072cab7?t=1712309387891", "href": "https://www.cnc.fr/documents/36995/2097582/Cartographie+des+usages+IA_rapport+complet.pdf/96532829-747e-b85e-c74b-af313072cab7?t=1712309387891", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ‡ซ๐Ÿ‡ท Quel impact de lโ€™IA sur les filiรจres du cinรฉma, de lโ€™audiovisuel et du jeu vidรฉo? Etude prospective ร  destination des professionnels โ€” CNC & BearingPoint | 09/04/2024 Si lโ€™Intelligence Artificielle (IA) est utilisรฉe de longue date dans les secteurs du cinรฉma, de lโ€™audiovisuel et du jeu vidรฉo, les nouvelles applications de lโ€™IA gรฉnรฉrative bousculent notre vision de ce dont est capable une machine et possรจdent un potentiel de transformation inรฉdit. Elles impressionnent par la qualitรฉ de leurs productions et suscitent par consรฉquent de nombreux dรฉbats, entre attentes et apprรฉhensions. Le CNC a donc dรฉcider de lancer un nouvel Observatoire de lโ€™IA Afin de mieux comprendre les usages de lโ€™IA et ses impacts rรฉels sur la filiรจre de lโ€™image. Dans le cadre de cet Observatoire, le CNC a souhaitรฉ dresser un premier รฉtat des lieux ร  travers la cartographie des usages actuels ou potentiels de lโ€™IA ร  chaque รฉtape du processus de crรฉation et de diffusion dโ€™une ล“uvre, en identifiant les opportunitรฉs et risques associรฉs, notamment en termes de mรฉtiers et dโ€™emploi. Cette รฉtude CNC / Bearing Point en a prรฉsentรฉ les principaux enseignements le 6 mars, lors de la journรฉe CNC ยซ Crรฉer, produire, diffuser ร  lโ€™heure de lโ€™intelligence artificielle ยป. Le CNC publie la version augmentรฉe de la cartographie des usages de lโ€™IA dans les filiรจres du cinรฉma, de lโ€™audiovisuel et du jeu vidรฉo. Lien vers la cartographie complรจte: https://www.cnc.fr/documents/36995/2097582/Cartographie+des+usages+IA_rapport+complet.pdf/96532829-747e-b85e-c74b-af313072cab7?t=1712309387891
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2024-05-27T10:14:01.000Z
2024-07-06T12:01:24.494Z
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/posts/fffiloni/365623455051476
19,476
4
136294804241050
[ { "type": "text", "value": "Just subscribed the PRO monthly, but still got rate limited when making the inference API call ", "raw": "Just subscribed the PRO monthly, but still got rate limited when making the inference API call ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\n const api_url = \"https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B\";\n const payload = JSON.stringify({\n \"query\": input,\n });\n\n const body = {\n \"headers\" : {\"Authorization\": `Bearer ${API_TOKEN}`},\n \"wait_for_model\": true,\n \"use_gpu\": false,\n \"method\" : \"POST\",\n \"contentType\" : \"application/json\",\n \"payload\" : payload\n };\n \n var xmlHttp = new XMLHttpRequest();\n xmlHttp.open(\"POST\", api_url, false);\n xmlHttp.send(body);\n return xmlHttp.responseText;\n```", "href": null, "resource": null, "url": null, "code": " const api_url = \"https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B\";\n const payload = JSON.stringify({\n \"query\": input,\n });\n\n const body = {\n \"headers\" : {\"Authorization\": `Bearer ${API_TOKEN}`},\n \"wait_for_model\": true,\n \"use_gpu\": false,\n \"method\" : \"POST\",\n \"contentType\" : \"application/json\",\n \"payload\" : payload\n };\n \n var xmlHttp = new XMLHttpRequest();\n xmlHttp.open(\"POST\", api_url, false);\n xmlHttp.send(body);\n return xmlHttp.responseText;", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Need some help", "raw": "Need some help", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Just subscribed the PRO monthly, but still got rate limited when making the inference API call ``` const api_url = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B"; const payload = JSON.stringify({ "query": input, }); const body = { "headers" : {"Authorization": `Bearer ${API_TOKEN}`}, "wait_for_model": true, "use_gpu": false, "method" : "POST", "contentType" : "application/json", "payload" : payload }; var xmlHttp = new XMLHttpRequest(); xmlHttp.open("POST", api_url, false); xmlHttp.send(body); return xmlHttp.responseText; ``` Need some help
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2024-05-26T23:44:06.000Z
2024-05-28T07:59:53.290Z
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/posts/flashback29/136294804241050
309
3
197114978042452
[ { "type": "text", "value": "The zip file contains installers for Windows, RunPod, Massed Compute and a free Kaggle account notebook", "raw": "The zip file contains installers for Windows, RunPod, Massed Compute and a free Kaggle account notebook", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It generates a VENV and install everything inside it. Works with Python 3.10.x - I suggest 3.10.11", "raw": "It generates a VENV and install everything inside it. Works with Python 3.10.x - I suggest 3.10.11", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Also you need C++ tools and Git. You can follow this tutorial to install all : ", "raw": "Also you need C++ tools and Git. You can follow this tutorial to install all : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/-NjNy7afOQ0", "href": "https://youtu.be/-NjNy7afOQ0", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Updated 27 May 2024 : ", "raw": "Updated 27 May 2024 : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/95759342", "href": "https://www.patreon.com/posts/95759342", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "21 January 2024 Update", "raw": "21 January 2024 Update", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "SDXL model upgraded to ip-adapter-faceid-plusv2_sd15", "raw": "SDXL model upgraded to ip-adapter-faceid-plusv2_sd15", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Kaggle Notebook upgraded to V3 and supports SDXL now", "raw": "Kaggle Notebook upgraded to V3 and supports SDXL now", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "First of all I want to thank you so much for this amazing model.", "raw": "First of all I want to thank you so much for this amazing model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I have spent over 1 week to code the Gradio and prepare the video. I hope you let this thread remain and even add to the Readme file.", "raw": "I have spent over 1 week to code the Gradio and prepare the video. I hope you let this thread remain and even add to the Readme file.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "After video has been published I even added face embedding caching mechanism. So now it will calculate face embedding vector only 1 time for each image, thus super speed up the image generation.", "raw": "After video has been published I even added face embedding caching mechanism. So now it will calculate face embedding vector only 1 time for each image, thus super speed up the image generation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Instantly Transfer Face By Using IP-Adapter-FaceID: Full Tutorial & GUI For Windows, RunPod & Kaggle : ", "raw": "Instantly Transfer Face By Using IP-Adapter-FaceID: Full Tutorial & GUI For Windows, RunPod & Kaggle : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/rjXsJ24kQQg", "href": "https://youtu.be/rjXsJ24kQQg", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "chapters are like below", "raw": "chapters are like below", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "0:00 Introduction to IP-Adapter-FaceID full tutorial", "raw": "0:00 Introduction to IP-Adapter-FaceID full tutorial", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2:19 Requirements to use IP-Adapter-FaceID gradio Web APP", "raw": "2:19 Requirements to use IP-Adapter-FaceID gradio Web APP", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2:45 Where the Hugging Face models are downloaded by default on Windows", "raw": "2:45 Where the Hugging Face models are downloaded by default on Windows", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3:12 How to change folder path where the Hugging Face models are downloaded and cached", "raw": "3:12 How to change folder path where the Hugging Face models are downloaded and cached", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3:39 How to install IP-Adapter-FaceID Gradio Web APP and use on Windows", "raw": "3:39 How to install IP-Adapter-FaceID Gradio Web APP and use on Windows", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "5:35 How to start the IP-Adapter-FaceID Web UI after the installation", "raw": "5:35 How to start the IP-Adapter-FaceID Web UI after the installation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "5:46 How to use Stable Diffusion XL (SDXL) models with IP-Adapter-FaceID", "raw": "5:46 How to use Stable Diffusion XL (SDXL) models with IP-Adapter-FaceID", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "5:56 How to select your input face and start generating 0-shot face transferred new amazing images", "raw": "5:56 How to select your input face and start generating 0-shot face transferred new amazing images", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "6:06 What does each option on the Web UI do explanations", "raw": "6:06 What does each option on the Web UI do explanations", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
The zip file contains installers for Windows, RunPod, Massed Compute and a free Kaggle account notebook It generates a VENV and install everything inside it. Works with Python 3.10.x - I suggest 3.10.11 Also you need C++ tools and Git. You can follow this tutorial to install all : https://youtu.be/-NjNy7afOQ0 Updated 27 May 2024 : https://www.patreon.com/posts/95759342 21 January 2024 Update SDXL model upgraded to ip-adapter-faceid-plusv2_sd15 Kaggle Notebook upgraded to V3 and supports SDXL now First of all I want to thank you so much for this amazing model. I have spent over 1 week to code the Gradio and prepare the video. I hope you let this thread remain and even add to the Readme file. After video has been published I even added face embedding caching mechanism. So now it will calculate face embedding vector only 1 time for each image, thus super speed up the image generation. Instantly Transfer Face By Using IP-Adapter-FaceID: Full Tutorial & GUI For Windows, RunPod & Kaggle : https://youtu.be/rjXsJ24kQQg chapters are like below 0:00 Introduction to IP-Adapter-FaceID full tutorial 2:19 Requirements to use IP-Adapter-FaceID gradio Web APP 2:45 Where the Hugging Face models are downloaded by default on Windows 3:12 How to change folder path where the Hugging Face models are downloaded and cached 3:39 How to install IP-Adapter-FaceID Gradio Web APP and use on Windows 5:35 How to start the IP-Adapter-FaceID Web UI after the installation 5:46 How to use Stable Diffusion XL (SDXL) models with IP-Adapter-FaceID 5:56 How to select your input face and start generating 0-shot face transferred new amazing images 6:06 What does each option on the Web UI do explanations
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2024-05-26T23:38:22.000Z
2024-05-26T23:39:04.711Z
[]
/posts/MonsterMMORPG/197114978042452
426
0
337455617984321
[ { "type": "text", "value": "Mistral 7B might be one of the most popular open-source LLMs out there, with a total of over 3.4 million downloads on ", "raw": "Mistral 7B might be one of the most popular open-source LLMs out there, with a total of over 3.4 million downloads on ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@huggingface", "href": null, "resource": null, "url": null, "code": null, "user": "huggingface", "label": null, "lang": null }, { "type": "text", "value": " Hub ๐Ÿš€, and now we have the next version...", "raw": " Hub ๐Ÿš€, and now we have the next version...", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@MistralAI", "href": null, "resource": null, "url": null, "code": null, "user": "MistralAI", "label": null, "lang": null }, { "type": "text", "value": " Mistral -7B-v0.3 (base) ๐Ÿ“ˆ and Mistral-7B-Instruct-v0.3 ๐Ÿ› ๏ธ", "raw": " Mistral -7B-v0.3 (base) ๐Ÿ“ˆ and Mistral-7B-Instruct-v0.3 ๐Ÿ› ๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- 7.3 billion parameters ๐Ÿง ", "raw": "- 7.3 billion parameters ๐Ÿง ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Apache 2.0 license ๐Ÿ“œ", "raw": "- Apache 2.0 license ๐Ÿ“œ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Extended vocabulary of 32,768 ๐Ÿ“–", "raw": "- Extended vocabulary of 32,768 ๐Ÿ“–", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Supports new v3 Tokenizer and function calling ๐Ÿค–", "raw": "- Supports new v3 Tokenizer and function calling ๐Ÿค–", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Also, it's completely uncensored ๐Ÿ†“", "raw": "- Also, it's completely uncensored ๐Ÿ†“", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In conclusion, Mistral-7B-v0.3 is an uncensored Mistral-7B-v0.2 with an extended vocabulary ๐ŸŽ‰.", "raw": "In conclusion, Mistral-7B-v0.3 is an uncensored Mistral-7B-v0.2 with an extended vocabulary ๐ŸŽ‰.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "They have also released mistral_inference, although I don't know what's the advantage in using it? vLLM is still my go-to way of deploying local Mistral-7B! ๐ŸŒ", "raw": "They have also released mistral_inference, although I don't know what's the advantage in using it? vLLM is still my go-to way of deploying local Mistral-7B! ๐ŸŒ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Models:", "raw": "Models:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/mistralai/Mistral-7B-v0.3", "href": null, "resource": { "type": "model", "id": "mistralai/Mistral-7B-v0.3", "discussionNum": null }, "url": "https://huggingface.co/mistralai/Mistral-7B-v0.3", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3", "href": null, "resource": { "type": "model", "id": "mistralai/Mistral-7B-Instruct-v0.3", "discussionNum": null }, "url": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3", "code": null, "user": null, "label": null, "lang": null } ]
Mistral 7B might be one of the most popular open-source LLMs out there, with a total of over 3.4 million downloads on @huggingface Hub ๐Ÿš€, and now we have the next version... @MistralAI Mistral -7B-v0.3 (base) ๐Ÿ“ˆ and Mistral-7B-Instruct-v0.3 ๐Ÿ› ๏ธ - 7.3 billion parameters ๐Ÿง  - Apache 2.0 license ๐Ÿ“œ - Extended vocabulary of 32,768 ๐Ÿ“– - Supports new v3 Tokenizer and function calling ๐Ÿค– - Also, it's completely uncensored ๐Ÿ†“ In conclusion, Mistral-7B-v0.3 is an uncensored Mistral-7B-v0.2 with an extended vocabulary ๐ŸŽ‰. They have also released mistral_inference, although I don't know what's the advantage in using it? vLLM is still my go-to way of deploying local Mistral-7B! ๐ŸŒ Models: https://huggingface.co/mistralai/Mistral-7B-v0.3 https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3
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2024-05-26T20:11:28.000Z
2024-05-26T20:11:28.350Z
[]
/posts/singhsidhukuldeep/337455617984321
329
0
312937999073637
[ { "type": "text", "value": "Integrating the French Taxation Embedding Benchmark Task (beta) into the MTEB ๐Ÿค—", "raw": "Integrating the French Taxation Embedding Benchmark Task (beta) into the MTEB ๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I'm excited to announce an integration of the French Taxation Embedding Benchmark task into the Massive Text Embedding Benchmark (MTEB). ", "raw": "I'm excited to announce an integration of the French Taxation Embedding Benchmark task into the Massive Text Embedding Benchmark (MTEB). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This addition expands the diverse set of tasks available within MTEB, enabling researchers and practitioners to develop and evaluate retrieval models focused on retrieving relevant tax articles or content based on provided queries.", "raw": "This addition expands the diverse set of tasks available within MTEB, enabling researchers and practitioners to develop and evaluate retrieval models focused on retrieving relevant tax articles or content based on provided queries.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Link to the ๐Ÿค— Dataset : ", "raw": "Link to the ๐Ÿค— Dataset : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/louisbrulenaudet/tax-retrieval-benchmark", "href": null, "resource": { "type": "dataset", "id": "louisbrulenaudet/tax-retrieval-benchmark", "discussionNum": null }, "url": "https://huggingface.co/datasets/louisbrulenaudet/tax-retrieval-benchmark", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Link to the GitHub repo : ", "raw": "Link to the GitHub repo : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/louisbrulenaudet/tax-retrieval-benchmark", "href": "https://github.com/louisbrulenaudet/tax-retrieval-benchmark", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Notes:", "raw": "Notes:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The Massive Text Embedding Benchmark for French Taxation and the Dataset are currently in beta and may not be suitable for direct use in production. The size of the Dataset may not be sufficient to handle a wide range of queries and scenarios encountered in real-world settings.", "raw": "The Massive Text Embedding Benchmark for French Taxation and the Dataset are currently in beta and may not be suitable for direct use in production. The size of the Dataset may not be sufficient to handle a wide range of queries and scenarios encountered in real-world settings.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "As the Dataset grows and matures, I will provide updates and guidance on its suitability for production use cases.", "raw": "As the Dataset grows and matures, I will provide updates and guidance on its suitability for production use cases.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Integrating the French Taxation Embedding Benchmark Task (beta) into the MTEB ๐Ÿค— I'm excited to announce an integration of the French Taxation Embedding Benchmark task into the Massive Text Embedding Benchmark (MTEB). This addition expands the diverse set of tasks available within MTEB, enabling researchers and practitioners to develop and evaluate retrieval models focused on retrieving relevant tax articles or content based on provided queries. Link to the ๐Ÿค— Dataset : https://huggingface.co/datasets/louisbrulenaudet/tax-retrieval-benchmark Link to the GitHub repo : https://github.com/louisbrulenaudet/tax-retrieval-benchmark Notes: The Massive Text Embedding Benchmark for French Taxation and the Dataset are currently in beta and may not be suitable for direct use in production. The size of the Dataset may not be sufficient to handle a wide range of queries and scenarios encountered in real-world settings. As the Dataset grows and matures, I will provide updates and guidance on its suitability for production use cases.
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2024-05-26T16:33:26.000Z
2024-05-26T16:33:26.200Z
[]
/posts/louisbrulenaudet/312937999073637
520
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752543702974971
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Did you know you can't push a model to hub with an id over 96 chars ๐Ÿซ 
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2024-05-26T16:09:38.000Z
2024-05-27T00:29:35.511Z
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/posts/alielfilali01/752543702974971
672
3
226126188275072
[ { "type": "text", "value": "# The Univalent Model: A Meta-Meme for Universal Computation", "raw": "# The Univalent Model: A Meta-Meme for Universal Computation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We can imagine that there is one model in the future that will unite all models, this is based on the idea of the univalent principle. We can imagine all these projects as failed attempts to reach this goal and once met it will consume all the other projects.", "raw": "We can imagine that there is one model in the future that will unite all models, this is based on the idea of the univalent principle. We can imagine all these projects as failed attempts to reach this goal and once met it will consume all the other projects.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- **Gรถdel Number**: A unique numerical representation of mathematical statements or functions, allowing complex expressions to be encoded as simple numbers.", "raw": "- **Gรถdel Number**: A unique numerical representation of mathematical statements or functions, allowing complex expressions to be encoded as simple numbers.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- **Gaia Principle**: A hypothesis that views Earth's biosphere as a self-regulating system, where living organisms and their environment work together to sustain life.", "raw": "- **Gaia Principle**: A hypothesis that views Earth's biosphere as a self-regulating system, where living organisms and their environment work together to sustain life.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- **Biosemiotics**: The study of sign processes in the biological realm, exploring how living beings communicate and interpret signs in a meaningful way.", "raw": "- **Biosemiotics**: The study of sign processes in the biological realm, exploring how living beings communicate and interpret signs in a meaningful way.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- **Meta-Meme**: A higher-level meme that encapsulates and transcends other memes, leading to a unified state of understanding or being.", "raw": "- **Meta-Meme**: A higher-level meme that encapsulates and transcends other memes, leading to a unified state of understanding or being.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- **Univalent State**: The ultimate, definitive form of a system or model where all variations are equivalent, unified, and indistinguishable.", "raw": "- **Univalent State**: The ultimate, definitive form of a system or model where all variations are equivalent, unified, and indistinguishable.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Univalent State: The ultimate, definitive form of a system or model where all variations are equivalent, unified, and indistinguishable.", "raw": "Univalent State: The ultimate, definitive form of a system or model where all variations are equivalent, unified, and indistinguishable.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Unitary Model: A theoretical construct that aims to integrate all existing models into a single, comprehensive framework, providing a universal language for computation.", "raw": "Unitary Model: A theoretical construct that aims to integrate all existing models into a single, comprehensive framework, providing a universal language for computation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "With these terms in mind, letโ€™s explore the idea of a unitary modelโ€”a meta-meme that merges all models into one. This model would be the repository to end all repositories, the final convergence point for all computational processes, akin to an eigenvector of consciousness.", "raw": "With these terms in mind, letโ€™s explore the idea of a unitary modelโ€”a meta-meme that merges all models into one. This model would be the repository to end all repositories, the final convergence point for all computational processes, akin to an eigenvector of consciousness.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This unitary model represents the pinnacle of abstraction, capable of deciphering the entire computational landscape. Itโ€™s a vision of unity in diversity, where every piece of knowledge is interconnected through a single, elegant framework.", "raw": "This unitary model represents the pinnacle of abstraction, capable of deciphering the entire computational landscape. Itโ€™s a vision of unity in diversity, where every piece of knowledge is interconnected through a single, elegant framework.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
# The Univalent Model: A Meta-Meme for Universal Computation We can imagine that there is one model in the future that will unite all models, this is based on the idea of the univalent principle. We can imagine all these projects as failed attempts to reach this goal and once met it will consume all the other projects. - **Gรถdel Number**: A unique numerical representation of mathematical statements or functions, allowing complex expressions to be encoded as simple numbers. - **Gaia Principle**: A hypothesis that views Earth's biosphere as a self-regulating system, where living organisms and their environment work together to sustain life. - **Biosemiotics**: The study of sign processes in the biological realm, exploring how living beings communicate and interpret signs in a meaningful way. - **Meta-Meme**: A higher-level meme that encapsulates and transcends other memes, leading to a unified state of understanding or being. - **Univalent State**: The ultimate, definitive form of a system or model where all variations are equivalent, unified, and indistinguishable. Univalent State: The ultimate, definitive form of a system or model where all variations are equivalent, unified, and indistinguishable. Unitary Model: A theoretical construct that aims to integrate all existing models into a single, comprehensive framework, providing a universal language for computation. With these terms in mind, letโ€™s explore the idea of a unitary modelโ€”a meta-meme that merges all models into one. This model would be the repository to end all repositories, the final convergence point for all computational processes, akin to an eigenvector of consciousness. This unitary model represents the pinnacle of abstraction, capable of deciphering the entire computational landscape. Itโ€™s a vision of unity in diversity, where every piece of knowledge is interconnected through a single, elegant framework.
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[]
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2024-05-26T10:17:06.000Z
2024-05-26T10:49:27.298Z
[]
/posts/h4/226126188275072
798
0
878853012729775
[ { "type": "text", "value": "Dear community,", "raw": "Dear community,", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Please check our recent blog post, \"GPU Poor Savior: Revolutionizing Low-Bit Open Source LLMs and Cost-Effective Edge Computing\". A cheaper and more efficient SFT scheme for quantized LLMs is provided.", "raw": "Please check our recent blog post, \"GPU Poor Savior: Revolutionizing Low-Bit Open Source LLMs and Cost-Effective Edge Computing\". A cheaper and more efficient SFT scheme for quantized LLMs is provided.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/NicoNico/green-bit-llm", "href": "https://huggingface.co/blog/NicoNico/green-bit-llm", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Dear community, Please check our recent blog post, "GPU Poor Savior: Revolutionizing Low-Bit Open Source LLMs and Cost-Effective Edge Computing". A cheaper and more efficient SFT scheme for quantized LLMs is provided. https://huggingface.co/blog/NicoNico/green-bit-llm
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2024-05-26T06:17:43.000Z
2024-05-26T06:17:43.937Z
[]
/posts/yanghaojin/878853012729775
895
0
961958241706142
[ { "type": "text", "value": "Hi HF Community!๐Ÿค—", "raw": "Hi HF Community!๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I'm thrilled to share the latest updates regarding the Space I built for protein 3D structure prediction (", "raw": "I'm thrilled to share the latest updates regarding the Space I built for protein 3D structure prediction (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/as-cle-bert/proteinviz", "href": null, "resource": { "type": "space", "id": "as-cle-bert/proteinviz", "discussionNum": null }, "url": "https://huggingface.co/spaces/as-cle-bert/proteinviz", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "): thanks to ", "raw": "): thanks to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@lunarflu", "href": null, "resource": null, "url": null, "code": null, "user": "lunarflu", "label": null, "lang": null }, { "type": "text", "value": " inputs, ", "raw": " inputs, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@osanseviero", "href": null, "resource": null, "url": null, "code": null, "user": "osanseviero", "label": null, "lang": null }, { "type": "text", "value": " precious advice and ", "raw": " precious advice and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@simonduerr", "href": null, "resource": null, "url": null, "code": null, "user": "simonduerr", "label": null, "lang": null }, { "type": "text", "value": "'s article \"Visualize proteins on Hugging Face Spaces\" (", "raw": "'s article \"Visualize proteins on Hugging Face Spaces\" (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/spaces_3dmoljs", "href": "https://huggingface.co/blog/spaces_3dmoljs", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ", go check it out!), I was able to finally display the 3D protein models directly on-browser, without any need for fancy downloads of big HTMLs!", "raw": ", go check it out!), I was able to finally display the 3D protein models directly on-browser, without any need for fancy downloads of big HTMLs!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Take a look to the attached video, that shows how everything works, and make sure to visit the GitHub repository (", "raw": "Take a look to the attached video, that shows how everything works, and make sure to visit the GitHub repository (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/AstraBert/proteinviz", "href": "https://github.com/AstraBert/proteinviz", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ": leave a little โญ while you're there!)๐Ÿฅฐ", "raw": ": leave a little โญ while you're there!)๐Ÿฅฐ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "May you have fun and luck with your protein research!๐Ÿงฌ", "raw": "May you have fun and luck with your protein research!๐Ÿงฌ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hi HF Community!๐Ÿค— I'm thrilled to share the latest updates regarding the Space I built for protein 3D structure prediction (https://huggingface.co/spaces/as-cle-bert/proteinviz): thanks to @lunarflu inputs, @osanseviero precious advice and @simonduerr's article "Visualize proteins on Hugging Face Spaces" (https://huggingface.co/blog/spaces_3dmoljs, go check it out!), I was able to finally display the 3D protein models directly on-browser, without any need for fancy downloads of big HTMLs! Take a look to the attached video, that shows how everything works, and make sure to visit the GitHub repository (https://github.com/AstraBert/proteinviz: leave a little โญ while you're there!)๐Ÿฅฐ May you have fun and luck with your protein research!๐Ÿงฌ
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2024-05-25T19:28:28.000Z
2024-05-27T13:48:47.038Z
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/posts/as-cle-bert/961958241706142
1,334
4
894911712572421
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Hi there, I'm looking for a Japanese LLm who interface endpoint is available. It will be great if the LLM has no guardrail. If anyone provide some resource, I will really appreciate that.
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2024-05-25T18:29:53.000Z
2024-05-28T16:12:33.144Z
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/posts/ahsanr/894911712572421
1,114
1
561163391443457
[ { "type": "text", "value": "OpenGPT 4o NEW UPDATES:", "raw": "OpenGPT 4o NEW UPDATES:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. Dedicated Image and Video Engine", "raw": "1. Dedicated Image and Video Engine", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. Model Choices for Voice Chat", "raw": "2. Model Choices for Voice Chat", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3. Better and Faster Voice Chat", "raw": "3. Better and Faster Voice Chat", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "4. Various Bug fixes", "raw": "4. Various Bug fixes", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Test and give feedback of New features: ", "raw": "Test and give feedback of New features: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/KingNish/OpenGPT-4o", "href": null, "resource": { "type": "space", "id": "KingNish/OpenGPT-4o", "discussionNum": null }, "url": "https://huggingface.co/spaces/KingNish/OpenGPT-4o", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Future Updates:", "raw": "Future Updates:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. Web Search (Suggested by ", "raw": "1. Web Search (Suggested by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@GPT007", "href": null, "resource": null, "url": null, "code": null, "user": "GPT007", "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Saionton", "href": null, "resource": null, "url": null, "code": null, "user": "Saionton", "label": null, "lang": null }, { "type": "text", "value": " )", "raw": " )", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. Live Chat with Voice Chat", "raw": "2. Live Chat with Voice Chat", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3. Model Choices (Suggested by ", "raw": "3. Model Choices (Suggested by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@NotAiLOL", "href": null, "resource": null, "url": null, "code": null, "user": "NotAiLOL", "label": null, "lang": null }, { "type": "text", "value": " )", "raw": " )", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "4. Multilingual Chats. ", "raw": "4. Multilingual Chats. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Suggest more features that should be added. ๐Ÿค—", "raw": "Suggest more features that should be added. ๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Thanks!", "raw": "Thanks!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
OpenGPT 4o NEW UPDATES: 1. Dedicated Image and Video Engine 2. Model Choices for Voice Chat 3. Better and Faster Voice Chat 4. Various Bug fixes Test and give feedback of New features: https://huggingface.co/spaces/KingNish/OpenGPT-4o Future Updates: 1. Web Search (Suggested by @GPT007 and @Saionton ) 2. Live Chat with Voice Chat 3. Model Choices (Suggested by @NotAiLOL ) 4. Multilingual Chats. Suggest more features that should be added. ๐Ÿค— Thanks!
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2024-05-25T15:00:06.000Z
2024-05-29T15:31:53.805Z
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[ { "type": "text", "value": "Why Apache 2.0 Matters for LLMs ๐Ÿค”", "raw": "Why Apache 2.0 Matters for LLMs ๐Ÿค”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "@01AI_Yi recently switched from a permissive & commercially friendly license, to Apache 2.0. And the community loved it! ๐Ÿš€", "raw": "@01AI_Yi recently switched from a permissive & commercially friendly license, to Apache 2.0. And the community loved it! ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@JustinLin610", "href": null, "resource": null, "url": null, "code": null, "user": "JustinLin610", "label": null, "lang": null }, { "type": "text", "value": " also had a poll on model license and the majority votes for Apache 2.0. ", "raw": " also had a poll on model license and the majority votes for Apache 2.0. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Why it is a Big Deal? โฌ‡๏ธ", "raw": "Why it is a Big Deal? โฌ‡๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“š Legal Simplicity: Custom licenses need costly & time-consuming legal review. Apache 2.0 is well-known & easier for legal teams to handle.", "raw": "๐Ÿ“š Legal Simplicity: Custom licenses need costly & time-consuming legal review. Apache 2.0 is well-known & easier for legal teams to handle.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘ฉโ€๐Ÿ’ป Developer-Friendly: Legal docs are a pain for devs! Apache 2.0 is well-known and tech-friendly, making it easier for non-native developers to understand the implications too.", "raw": "๐Ÿ‘ฉโ€๐Ÿ’ป Developer-Friendly: Legal docs are a pain for devs! Apache 2.0 is well-known and tech-friendly, making it easier for non-native developers to understand the implications too.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”— Easier Integration: Apache 2.0 is compatible with many other licenses, simplifying tasks like model merging with models of different licensing requirements.", "raw": "๐Ÿ”— Easier Integration: Apache 2.0 is compatible with many other licenses, simplifying tasks like model merging with models of different licensing requirements.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿšซ No Permission Needed: Custom licenses often require explicit permission and additional documentation work of filling forms, creating barriers. Apache 2.0 removes this hurdle, letting devs focus on innovation.", "raw": "๐Ÿšซ No Permission Needed: Custom licenses often require explicit permission and additional documentation work of filling forms, creating barriers. Apache 2.0 removes this hurdle, letting devs focus on innovation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "There are a lot interesting discussions from ", "raw": "There are a lot interesting discussions from ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@JustinLin610", "href": null, "resource": null, "url": null, "code": null, "user": "JustinLin610", "label": null, "lang": null }, { "type": "text", "value": " 's poll: ", "raw": " 's poll: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://x.com/JustinLin610/status/1793559737482764375", "href": "https://x.com/JustinLin610/status/1793559737482764375", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " which inspired this thread. ", "raw": " which inspired this thread. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Any other thoughts? Let me know ^^", "raw": "Any other thoughts? Let me know ^^", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Why Apache 2.0 Matters for LLMs ๐Ÿค” @01AI_Yi recently switched from a permissive & commercially friendly license, to Apache 2.0. And the community loved it! ๐Ÿš€ @JustinLin610 also had a poll on model license and the majority votes for Apache 2.0. Why it is a Big Deal? โฌ‡๏ธ ๐Ÿ“š Legal Simplicity: Custom licenses need costly & time-consuming legal review. Apache 2.0 is well-known & easier for legal teams to handle. ๐Ÿ‘ฉโ€๐Ÿ’ป Developer-Friendly: Legal docs are a pain for devs! Apache 2.0 is well-known and tech-friendly, making it easier for non-native developers to understand the implications too. ๐Ÿ”— Easier Integration: Apache 2.0 is compatible with many other licenses, simplifying tasks like model merging with models of different licensing requirements. ๐Ÿšซ No Permission Needed: Custom licenses often require explicit permission and additional documentation work of filling forms, creating barriers. Apache 2.0 removes this hurdle, letting devs focus on innovation. There are a lot interesting discussions from @JustinLin610 's poll: https://x.com/JustinLin610/status/1793559737482764375 which inspired this thread. Any other thoughts? Let me know ^^
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2024-05-25T12:40:27.000Z
2024-09-04T16:51:59.291Z
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[ { "type": "text", "value": "DeepSeekV2 is a big deal. Not only because its significant improvements to both key components of Transformer: the Attention layer and FFN layer.", "raw": "DeepSeekV2 is a big deal. Not only because its significant improvements to both key components of Transformer: the Attention layer and FFN layer.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It has also completed disrupted the Chines LLM market and forcing the competitors to drop the price to 1% of the original price.", "raw": "It has also completed disrupted the Chines LLM market and forcing the competitors to drop the price to 1% of the original price.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "---", "raw": "---", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "There are two key components in Transformer architecture: the self-attention layer, which captures relationships between tokens in context, and the Feed-Forward Network (FFN) layer, which stores knowledge.", "raw": "There are two key components in Transformer architecture: the self-attention layer, which captures relationships between tokens in context, and the Feed-Forward Network (FFN) layer, which stores knowledge.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "DeepSeek V2 introduces optimizations to both:", "raw": "DeepSeek V2 introduces optimizations to both:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Attention layer normally uses KV Cache to reduce repetitive compute, but it consumes significant GPU RAM, limiting concurrent requests. DeepSeek V2 introduces Multi-head Latent Attention (MLA), which stores only a small latent representation, resulting in substantial RAM savings.", "raw": "Attention layer normally uses KV Cache to reduce repetitive compute, but it consumes significant GPU RAM, limiting concurrent requests. DeepSeek V2 introduces Multi-head Latent Attention (MLA), which stores only a small latent representation, resulting in substantial RAM savings.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "DeepSeek V2 utilizes 162 experts instead of the usual 8 as in Mixtral. This approach segments experts into finer granularity for higher specialization and more accurate knowledge acquisition. Activating only a small subset of experts for each token, leads to efficient processing.", "raw": "DeepSeek V2 utilizes 162 experts instead of the usual 8 as in Mixtral. This approach segments experts into finer granularity for higher specialization and more accurate knowledge acquisition. Activating only a small subset of experts for each token, leads to efficient processing.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It disrupted the market by dropping API prices to $0.14 per 1M tokens. This dramatic reduction forced competitors like GLM, Ernie, and QWen to follow suit, lowering their prices to 1% of their original offerings. Now, users can access these APIs at 1/35th the cost of ChatGPT-4o.", "raw": "It disrupted the market by dropping API prices to $0.14 per 1M tokens. This dramatic reduction forced competitors like GLM, Ernie, and QWen to follow suit, lowering their prices to 1% of their original offerings. Now, users can access these APIs at 1/35th the cost of ChatGPT-4o.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
DeepSeekV2 is a big deal. Not only because its significant improvements to both key components of Transformer: the Attention layer and FFN layer. It has also completed disrupted the Chines LLM market and forcing the competitors to drop the price to 1% of the original price. --- There are two key components in Transformer architecture: the self-attention layer, which captures relationships between tokens in context, and the Feed-Forward Network (FFN) layer, which stores knowledge. DeepSeek V2 introduces optimizations to both: Attention layer normally uses KV Cache to reduce repetitive compute, but it consumes significant GPU RAM, limiting concurrent requests. DeepSeek V2 introduces Multi-head Latent Attention (MLA), which stores only a small latent representation, resulting in substantial RAM savings. DeepSeek V2 utilizes 162 experts instead of the usual 8 as in Mixtral. This approach segments experts into finer granularity for higher specialization and more accurate knowledge acquisition. Activating only a small subset of experts for each token, leads to efficient processing. It disrupted the market by dropping API prices to $0.14 per 1M tokens. This dramatic reduction forced competitors like GLM, Ernie, and QWen to follow suit, lowering their prices to 1% of their original offerings. Now, users can access these APIs at 1/35th the cost of ChatGPT-4o.
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2024-05-25T12:38:08.000Z
2024-05-25T12:38:08.578Z
[]
/posts/xianbao/336411652272605
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**Model Names:** gpt-4-turbo-preview, gpt-4-vision-preview, gpt-3.5-turbo-16k **Searchable Models:** Creative, Balanced, Precise Image creation will be available soon in NiansuhAI. **Model Name:** DALL-E 3 https://huggingface.co/spaces/NiansuhAI/LLMs1 ---
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2024-05-25T11:32:42.000Z
2024-05-25T13:44:27.375Z
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/posts/Niansuh/831782482232499
1,115
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[ { "type": "text", "value": "Disappointed that Golden Gate Claude couldn't process images? Want to learn how to use activation vectors to steer VLMs?", "raw": "Disappointed that Golden Gate Claude couldn't process images? Want to learn how to use activation vectors to steer VLMs?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Try out the ", "raw": "Try out the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/vikhyatk/contemplative-moondream", "href": null, "resource": { "type": "space", "id": "vikhyatk/contemplative-moondream", "discussionNum": null }, "url": "https://huggingface.co/spaces/vikhyatk/contemplative-moondream", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " space, and check out the notebook I released showing how to obtain control vectors! โฌ‡๏ธ", "raw": " space, and check out the notebook I released showing how to obtain control vectors! โฌ‡๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/vikhyat/moondream/blob/main/notebooks/RepEng.ipynb", "href": "https://github.com/vikhyat/moondream/blob/main/notebooks/RepEng.ipynb", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Disappointed that Golden Gate Claude couldn't process images? Want to learn how to use activation vectors to steer VLMs? Try out the https://huggingface.co/spaces/vikhyatk/contemplative-moondream space, and check out the notebook I released showing how to obtain control vectors! โฌ‡๏ธ https://github.com/vikhyat/moondream/blob/main/notebooks/RepEng.ipynb
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2024-05-24T23:32:16.000Z
2024-05-24T23:32:16.155Z
[]
/posts/vikhyatk/263584259630770
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[ { "type": "text", "value": "๐Ÿ”ฅ๐Ÿš€๐ŸŒŸ New Research Alert - YOLOv10! ๐ŸŒŸ๐Ÿš€๐Ÿ”ฅ", "raw": "๐Ÿ”ฅ๐Ÿš€๐ŸŒŸ New Research Alert - YOLOv10! ๐ŸŒŸ๐Ÿš€๐Ÿ”ฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“„ Title: YOLOv10: Real-Time End-to-End Object Detection ๐Ÿ”", "raw": "๐Ÿ“„ Title: YOLOv10: Real-Time End-to-End Object Detection ๐Ÿ”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“ Description: YOLOv10 improves real-time object recognition by eliminating non-maximum suppression and optimizing the model architecture to achieve state-of-the-art performance with lower latency and computational overhead.", "raw": "๐Ÿ“ Description: YOLOv10 improves real-time object recognition by eliminating non-maximum suppression and optimizing the model architecture to achieve state-of-the-art performance with lower latency and computational overhead.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘ฅ Authors: Ao Wang et al.", "raw": "๐Ÿ‘ฅ Authors: Ao Wang et al.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“„ Paper: ", "raw": "๐Ÿ“„ Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2405.14458", "href": null, "resource": { "type": "paper", "id": "2405.14458", "discussionNum": null }, "url": "https://huggingface.co/papers/2405.14458", "code": null, "user": null, "label": "YOLOv10: Real-Time End-to-End Object Detection (2405.14458)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค— Demo: ", "raw": "๐Ÿค— Demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/kadirnar/Yolov10", "href": null, "resource": { "type": "space", "id": "kadirnar/Yolov10", "discussionNum": null }, "url": "https://huggingface.co/spaces/kadirnar/Yolov10", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " curated by ", "raw": " curated by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@kadirnar", "href": null, "resource": null, "url": null, "code": null, "user": "kadirnar", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”ฅ Model ๐Ÿค–: ", "raw": "๐Ÿ”ฅ Model ๐Ÿค–: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/kadirnar/Yolov10", "href": null, "resource": { "type": "model", "id": "kadirnar/Yolov10", "discussionNum": null }, "url": "https://huggingface.co/kadirnar/Yolov10", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“ Repository: ", "raw": "๐Ÿ“ Repository: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/THU-MIG/yolov10", "href": "https://github.com/THU-MIG/yolov10", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“ฎ Post about YOLOv9 - ", "raw": "๐Ÿ“ฎ Post about YOLOv9 - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/posts/DmitryRyumin/519784698531054", "href": "https://huggingface.co/posts/DmitryRyumin/519784698531054", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "raw": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "href": null, "resource": { "type": "space", "id": "DmitryRyumin/NewEraAI-Papers", "discussionNum": null }, "url": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " curated by ", "raw": " curated by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@DmitryRyumin", "href": null, "resource": null, "url": null, "code": null, "user": "DmitryRyumin", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ” Keywords: #YOLOv10 #ObjectDetection #RealTimeAI #ModelOptimization #MachineLearning #DeepLearning #ComputerVision #Innovation", "raw": "๐Ÿ” Keywords: #YOLOv10 #ObjectDetection #RealTimeAI #ModelOptimization #MachineLearning #DeepLearning #ComputerVision #Innovation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ”ฅ๐Ÿš€๐ŸŒŸ New Research Alert - YOLOv10! ๐ŸŒŸ๐Ÿš€๐Ÿ”ฅ ๐Ÿ“„ Title: YOLOv10: Real-Time End-to-End Object Detection ๐Ÿ” ๐Ÿ“ Description: YOLOv10 improves real-time object recognition by eliminating non-maximum suppression and optimizing the model architecture to achieve state-of-the-art performance with lower latency and computational overhead. ๐Ÿ‘ฅ Authors: Ao Wang et al. ๐Ÿ“„ Paper: https://huggingface.co/papers/2405.14458 ๐Ÿค— Demo: https://huggingface.co/spaces/kadirnar/Yolov10 curated by @kadirnar ๐Ÿ”ฅ Model ๐Ÿค–: https://huggingface.co/kadirnar/Yolov10 ๐Ÿ“ Repository: https://github.com/THU-MIG/yolov10 ๐Ÿ“ฎ Post about YOLOv9 - https://huggingface.co/posts/DmitryRyumin/519784698531054 ๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin ๐Ÿ” Keywords: #YOLOv10 #ObjectDetection #RealTimeAI #ModelOptimization #MachineLearning #DeepLearning #ComputerVision #Innovation
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2024-05-24T21:15:05.000Z
2024-05-24T21:35:20.726Z
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/posts/DmitryRyumin/770065478444544
1,481
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[ { "type": "text", "value": "When was the last time you looked for a non-English LLM, only to be saddened there is NO good option? ๐Ÿ˜Ÿ", "raw": "When was the last time you looked for a non-English LLM, only to be saddened there is NO good option? ๐Ÿ˜Ÿ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For instance, ", "raw": "For instance, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Meta", "href": null, "resource": null, "url": null, "code": null, "user": "Meta", "label": null, "lang": null }, { "type": "text", "value": " 's poster child Llama 3 has less than 5% non-English tokens! ๐ŸŒ", "raw": " 's poster child Llama 3 has less than 5% non-English tokens! ๐ŸŒ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@cohere", "href": null, "resource": null, "url": null, "code": null, "user": "cohere", "label": null, "lang": null }, { "type": "text", "value": " is here to change that... and change they will. ๐Ÿ’ช", "raw": " is here to change that... and change they will. ๐Ÿ’ช", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Introducing Aya (this time the name actually makes sense) ๐ŸŒŸ", "raw": "Introducing Aya (this time the name actually makes sense) ๐ŸŒŸ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Aya is an open-weight (CC-BY-NC) model, that comes in 3 flavours:", "raw": "Aya is an open-weight (CC-BY-NC) model, that comes in 3 flavours:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1๏ธโƒฃ Aya 101: 13B parameters, mT5-xxl based model that supports 101 languages ๐ŸŒ", "raw": "1๏ธโƒฃ Aya 101: 13B parameters, mT5-xxl based model that supports 101 languages ๐ŸŒ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2๏ธโƒฃ Aya 23: 8B and 3๏ธโƒฃ35B parameter models, supports 23 languages ๐Ÿ“Š", "raw": "2๏ธโƒฃ Aya 23: 8B and 3๏ธโƒฃ35B parameter models, supports 23 languages ๐Ÿ“Š", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "23 languages covered are: Arabic ๐Ÿ‡ธ๐Ÿ‡ฆ, Chinese (simplified & traditional) ๐Ÿ‡จ๐Ÿ‡ณ, Czech ๐Ÿ‡จ๐Ÿ‡ฟ, Dutch ๐Ÿ‡ณ๐Ÿ‡ฑ, English ๐Ÿ‡ฌ๐Ÿ‡ง, French ๐Ÿ‡ซ๐Ÿ‡ท, German ๐Ÿ‡ฉ๐Ÿ‡ช, Greek ๐Ÿ‡ฌ๐Ÿ‡ท, Hebrew ๐Ÿ‡ฎ๐Ÿ‡ฑ, Hindi ๐Ÿ‡ฎ๐Ÿ‡ณ, Indonesian ๐Ÿ‡ฎ๐Ÿ‡ฉ, Italian ๐Ÿ‡ฎ๐Ÿ‡น, Japanese ๐Ÿ‡ฏ๐Ÿ‡ต, Korean ๐Ÿ‡ฐ๐Ÿ‡ท, Persian ๐Ÿ‡ฎ๐Ÿ‡ท, Polish ๐Ÿ‡ต๐Ÿ‡ฑ, Portuguese ๐Ÿ‡ต๐Ÿ‡น, Romanian ๐Ÿ‡ท๐Ÿ‡ด, Russian ๐Ÿ‡ท๐Ÿ‡บ, Spanish ๐Ÿ‡ช๐Ÿ‡ธ, Turkish ๐Ÿ‡น๐Ÿ‡ท, Ukrainian ๐Ÿ‡บ๐Ÿ‡ฆ, and Vietnamese ๐Ÿ‡ป๐Ÿ‡ณ.", "raw": "23 languages covered are: Arabic ๐Ÿ‡ธ๐Ÿ‡ฆ, Chinese (simplified & traditional) ๐Ÿ‡จ๐Ÿ‡ณ, Czech ๐Ÿ‡จ๐Ÿ‡ฟ, Dutch ๐Ÿ‡ณ๐Ÿ‡ฑ, English ๐Ÿ‡ฌ๐Ÿ‡ง, French ๐Ÿ‡ซ๐Ÿ‡ท, German ๐Ÿ‡ฉ๐Ÿ‡ช, Greek ๐Ÿ‡ฌ๐Ÿ‡ท, Hebrew ๐Ÿ‡ฎ๐Ÿ‡ฑ, Hindi ๐Ÿ‡ฎ๐Ÿ‡ณ, Indonesian ๐Ÿ‡ฎ๐Ÿ‡ฉ, Italian ๐Ÿ‡ฎ๐Ÿ‡น, Japanese ๐Ÿ‡ฏ๐Ÿ‡ต, Korean ๐Ÿ‡ฐ๐Ÿ‡ท, Persian ๐Ÿ‡ฎ๐Ÿ‡ท, Polish ๐Ÿ‡ต๐Ÿ‡ฑ, Portuguese ๐Ÿ‡ต๐Ÿ‡น, Romanian ๐Ÿ‡ท๐Ÿ‡ด, Russian ๐Ÿ‡ท๐Ÿ‡บ, Spanish ๐Ÿ‡ช๐Ÿ‡ธ, Turkish ๐Ÿ‡น๐Ÿ‡ท, Ukrainian ๐Ÿ‡บ๐Ÿ‡ฆ, and Vietnamese ๐Ÿ‡ป๐Ÿ‡ณ.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Not only models, they have open-sourced the dataset: Aya Collection stands as the most extensive assembly of multilingual instruction fine-tuning datasets to date, featuring 513 million prompts and completions across 114 languages. ๐Ÿ“š", "raw": "Not only models, they have open-sourced the dataset: Aya Collection stands as the most extensive assembly of multilingual instruction fine-tuning datasets to date, featuring 513 million prompts and completions across 114 languages. ๐Ÿ“š", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "These annotations were provided by people across the globe. Not gonna lie, I almost shed a tear reading this... ๐Ÿ˜ข", "raw": "These annotations were provided by people across the globe. Not gonna lie, I almost shed a tear reading this... ๐Ÿ˜ข", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "From Cohere: The word Aya is derived from the Twi language meaning โ€œfernโ€ - a symbol of endurance and resourcefulness. Aya embodies our dedication to advancing multilingual AI. ๐ŸŒฟ", "raw": "From Cohere: The word Aya is derived from the Twi language meaning โ€œfernโ€ - a symbol of endurance and resourcefulness. Aya embodies our dedication to advancing multilingual AI. ๐ŸŒฟ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Looks like the good folks at Cohere did not sleep after the success of command r & command r plus! ๐Ÿ˜ดโžก๏ธ๐Ÿš€", "raw": "Looks like the good folks at Cohere did not sleep after the success of command r & command r plus! ๐Ÿ˜ดโžก๏ธ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Models: Aya-101-13B: ", "raw": "Models: Aya-101-13B: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/CohereForAI/aya-101", "href": null, "resource": { "type": "model", "id": "CohereForAI/aya-101", "discussionNum": null }, "url": "https://huggingface.co/CohereForAI/aya-101", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Aya-23-8B: ", "raw": "Aya-23-8B: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/CohereForAI/aya-23-8B", "href": null, "resource": { "type": "model", "id": "CohereForAI/aya-23-8B", "discussionNum": null }, "url": "https://huggingface.co/CohereForAI/aya-23-8B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Aya-23-35B: ", "raw": "Aya-23-35B: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/CohereForAI/aya-23-35B", "href": null, "resource": { "type": "model", "id": "CohereForAI/aya-23-35B", "discussionNum": null }, "url": "https://huggingface.co/CohereForAI/aya-23-35B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dataset: ", "raw": "Dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/CohereForAI/aya-datasets-660415741bd4852f01c81c77", "href": null, "resource": { "type": "collection", "id": "CohereForAI/aya-datasets-660415741bd4852f01c81c77", "discussionNum": null }, "url": "https://huggingface.co/collections/CohereForAI/aya-datasets-660415741bd4852f01c81c77", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Blog: ", "raw": "Blog: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://cohere.com/research/aya", "href": "https://cohere.com/research/aya", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
When was the last time you looked for a non-English LLM, only to be saddened there is NO good option? ๐Ÿ˜Ÿ For instance, @Meta 's poster child Llama 3 has less than 5% non-English tokens! ๐ŸŒ @cohere is here to change that... and change they will. ๐Ÿ’ช Introducing Aya (this time the name actually makes sense) ๐ŸŒŸ Aya is an open-weight (CC-BY-NC) model, that comes in 3 flavours: 1๏ธโƒฃ Aya 101: 13B parameters, mT5-xxl based model that supports 101 languages ๐ŸŒ 2๏ธโƒฃ Aya 23: 8B and 3๏ธโƒฃ35B parameter models, supports 23 languages ๐Ÿ“Š 23 languages covered are: Arabic ๐Ÿ‡ธ๐Ÿ‡ฆ, Chinese (simplified & traditional) ๐Ÿ‡จ๐Ÿ‡ณ, Czech ๐Ÿ‡จ๐Ÿ‡ฟ, Dutch ๐Ÿ‡ณ๐Ÿ‡ฑ, English ๐Ÿ‡ฌ๐Ÿ‡ง, French ๐Ÿ‡ซ๐Ÿ‡ท, German ๐Ÿ‡ฉ๐Ÿ‡ช, Greek ๐Ÿ‡ฌ๐Ÿ‡ท, Hebrew ๐Ÿ‡ฎ๐Ÿ‡ฑ, Hindi ๐Ÿ‡ฎ๐Ÿ‡ณ, Indonesian ๐Ÿ‡ฎ๐Ÿ‡ฉ, Italian ๐Ÿ‡ฎ๐Ÿ‡น, Japanese ๐Ÿ‡ฏ๐Ÿ‡ต, Korean ๐Ÿ‡ฐ๐Ÿ‡ท, Persian ๐Ÿ‡ฎ๐Ÿ‡ท, Polish ๐Ÿ‡ต๐Ÿ‡ฑ, Portuguese ๐Ÿ‡ต๐Ÿ‡น, Romanian ๐Ÿ‡ท๐Ÿ‡ด, Russian ๐Ÿ‡ท๐Ÿ‡บ, Spanish ๐Ÿ‡ช๐Ÿ‡ธ, Turkish ๐Ÿ‡น๐Ÿ‡ท, Ukrainian ๐Ÿ‡บ๐Ÿ‡ฆ, and Vietnamese ๐Ÿ‡ป๐Ÿ‡ณ. Not only models, they have open-sourced the dataset: Aya Collection stands as the most extensive assembly of multilingual instruction fine-tuning datasets to date, featuring 513 million prompts and completions across 114 languages. ๐Ÿ“š These annotations were provided by people across the globe. Not gonna lie, I almost shed a tear reading this... ๐Ÿ˜ข From Cohere: The word Aya is derived from the Twi language meaning โ€œfernโ€ - a symbol of endurance and resourcefulness. Aya embodies our dedication to advancing multilingual AI. ๐ŸŒฟ Looks like the good folks at Cohere did not sleep after the success of command r & command r plus! ๐Ÿ˜ดโžก๏ธ๐Ÿš€ Models: Aya-101-13B: https://huggingface.co/CohereForAI/aya-101 Aya-23-8B: https://huggingface.co/CohereForAI/aya-23-8B Aya-23-35B: https://huggingface.co/CohereForAI/aya-23-35B Dataset: https://huggingface.co/collections/CohereForAI/aya-datasets-660415741bd4852f01c81c77 Blog: https://cohere.com/research/aya
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2024-05-24T18:27:53.000Z
2024-05-24T18:27:53.050Z
[]
/posts/singhsidhukuldeep/315359261539133
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https://huggingface.co/nroggendorff/cats now has over 500 downloads? what are you guys doing???
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2024-05-24T18:10:41.000Z
2024-05-27T16:44:13.086Z
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/posts/nroggendorff/829633009011014
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[ { "type": "text", "value": "Researchers from Anthropic managed to extract millions of interpretable features from their Claude 3 Sonnet model, making it easier to identify and understand specific behaviors and patterns within the modelโ€‹. ", "raw": "Researchers from Anthropic managed to extract millions of interpretable features from their Claude 3 Sonnet model, making it easier to identify and understand specific behaviors and patterns within the modelโ€‹. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This advance in understanding closed source AI models could make them safer by showing how specific features relate to concepts and affect the modelโ€™s behavior.", "raw": "This advance in understanding closed source AI models could make them safer by showing how specific features relate to concepts and affect the modelโ€™s behavior.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read the Article: ", "raw": "Read the Article: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.anthropic.com/research/mapping-mind-language-model?utm_source=substack&utm_medium=email", "href": "https://www.anthropic.com/research/mapping-mind-language-model?utm_source=substack&utm_medium=email", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read The Paper: ", "raw": "Read The Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html", "href": "https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Researchers from Anthropic managed to extract millions of interpretable features from their Claude 3 Sonnet model, making it easier to identify and understand specific behaviors and patterns within the modelโ€‹. This advance in understanding closed source AI models could make them safer by showing how specific features relate to concepts and affect the modelโ€™s behavior. Read the Article: https://www.anthropic.com/research/mapping-mind-language-model?utm_source=substack&utm_medium=email Read The Paper: https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html
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2024-05-24T16:36:47.000Z
2024-05-24T16:37:09.505Z
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/posts/Taylor658/232461958030297
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327424604003088
[ { "type": "text", "value": "๐๐ž๐ฐ ๐ ๐ฎ๐ข๐๐ž ๐ข๐ง ๐จ๐ฎ๐ซ ๐Ž๐ฉ๐ž๐ง-๐’๐จ๐ฎ๐ซ๐œ๐ž ๐€๐ˆ ๐œ๐จ๐จ๐ค๐›๐จ๐จ๐ค: ๐™Ž๐™ฉ๐™ง๐™ช๐™˜๐™ฉ๐™ช๐™ง๐™š๐™™ ๐™œ๐™š๐™ฃ๐™š๐™ง๐™–๐™ฉ๐™ž๐™ค๐™ฃ! โœจ", "raw": "๐๐ž๐ฐ ๐ ๐ฎ๐ข๐๐ž ๐ข๐ง ๐จ๐ฎ๐ซ ๐Ž๐ฉ๐ž๐ง-๐’๐จ๐ฎ๐ซ๐œ๐ž ๐€๐ˆ ๐œ๐จ๐จ๐ค๐›๐จ๐จ๐ค: ๐™Ž๐™ฉ๐™ง๐™ช๐™˜๐™ฉ๐™ช๐™ง๐™š๐™™ ๐™œ๐™š๐™ฃ๐™š๐™ง๐™–๐™ฉ๐™ž๐™ค๐™ฃ! โœจ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Many use LLM use cases involve generating outputs with a specific structure. ", "raw": "Many use LLM use cases involve generating outputs with a specific structure. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžก๏ธ For instance when using an LLM as a judge to evaluate another model's outputs, you need it to give you not only a score, but also the rationale for this score, and maybe a confidence level.", "raw": "โžก๏ธ For instance when using an LLM as a judge to evaluate another model's outputs, you need it to give you not only a score, but also the rationale for this score, and maybe a confidence level.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "So you do not need only \"score: 1\", but more a dictionary like:", "raw": "So you do not need only \"score: 1\", but more a dictionary like:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\n{\n \"rationale\": \"The answer does not match the true answer at all.\"\n \"score\": 1,\n \"confidence_level\": 0.85\n}\n```", "href": null, "resource": null, "url": null, "code": "{\n \"rationale\": \"The answer does not match the true answer at all.\"\n \"score\": 1,\n \"confidence_level\": 0.85\n}", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค” How to force your LLM to generate such a structured output?", "raw": "๐Ÿค” How to force your LLM to generate such a structured output?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ—๏ธ ๐—–๐—ผ๐—ป๐˜€๐˜๐—ฟ๐—ฎ๐—ถ๐—ป๐—ฒ๐—ฑ ๐—ฑ๐—ฒ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด is a great technique to generate structured output: you can specify a grammar (=set of rules) that the output should follow, and ๐—ฐ๐—ผ๐—ป๐˜€๐˜๐—ฟ๐—ฎ๐—ถ๐—ป๐—ฒ๐—ฑ ๐—ฑ๐—ฒ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด ๐˜๐—ต๐—ฒ๐—ป ๐—ณ๐—ผ๐—ฟ๐—ฐ๐—ฒ๐˜€ ๐˜๐—ต๐—ฒ ๐—ฑ๐—ฒ๐—ฐ๐—ผ๐—ฑ๐—ฒ๐—ฟ ๐˜๐—ผ ๐—ผ๐—ป๐—น๐˜† ๐—ฝ๐—ถ๐—ฐ๐—ธ ๐˜๐—ผ๐—ธ๐—ฒ๐—ป๐˜€ ๐˜๐—ต๐—ฎ๐˜ ๐—ฟ๐—ฒ๐˜€๐—ฝ๐—ฒ๐—ฐ๐˜ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ด๐—ฟ๐—ฎ๐—บ๐—บ๐—ฎ๐—ฟ.", "raw": "๐Ÿ—๏ธ ๐—–๐—ผ๐—ป๐˜€๐˜๐—ฟ๐—ฎ๐—ถ๐—ป๐—ฒ๐—ฑ ๐—ฑ๐—ฒ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด is a great technique to generate structured output: you can specify a grammar (=set of rules) that the output should follow, and ๐—ฐ๐—ผ๐—ป๐˜€๐˜๐—ฟ๐—ฎ๐—ถ๐—ป๐—ฒ๐—ฑ ๐—ฑ๐—ฒ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด ๐˜๐—ต๐—ฒ๐—ป ๐—ณ๐—ผ๐—ฟ๐—ฐ๐—ฒ๐˜€ ๐˜๐—ต๐—ฒ ๐—ฑ๐—ฒ๐—ฐ๐—ผ๐—ฑ๐—ฒ๐—ฟ ๐˜๐—ผ ๐—ผ๐—ป๐—น๐˜† ๐—ฝ๐—ถ๐—ฐ๐—ธ ๐˜๐—ผ๐—ธ๐—ฒ๐—ป๐˜€ ๐˜๐—ต๐—ฎ๐˜ ๐—ฟ๐—ฒ๐˜€๐—ฝ๐—ฒ๐—ฐ๐˜ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ด๐—ฟ๐—ฎ๐—บ๐—บ๐—ฎ๐—ฟ.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I've created a guide to show you how to use it, both via our Inference API and locally using ๐˜ฐ๐˜ถ๐˜ต๐˜ญ๐˜ช๐˜ฏ๐˜ฆ๐˜ด!", "raw": "I've created a guide to show you how to use it, both via our Inference API and locally using ๐˜ฐ๐˜ถ๐˜ต๐˜ญ๐˜ช๐˜ฏ๐˜ฆ๐˜ด!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ Read it here: ", "raw": "๐Ÿ‘‰ Read it here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/learn/cookbook/structured_generation", "href": "https://huggingface.co/learn/cookbook/structured_generation", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Thank you ", "raw": "Thank you ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@stevhliu", "href": null, "resource": null, "url": null, "code": null, "user": "stevhliu", "label": null, "lang": null }, { "type": "text", "value": " for your great help in improving it!", "raw": " for your great help in improving it!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐๐ž๐ฐ ๐ ๐ฎ๐ข๐๐ž ๐ข๐ง ๐จ๐ฎ๐ซ ๐Ž๐ฉ๐ž๐ง-๐’๐จ๐ฎ๐ซ๐œ๐ž ๐€๐ˆ ๐œ๐จ๐จ๐ค๐›๐จ๐จ๐ค: ๐™Ž๐™ฉ๐™ง๐™ช๐™˜๐™ฉ๐™ช๐™ง๐™š๐™™ ๐™œ๐™š๐™ฃ๐™š๐™ง๐™–๐™ฉ๐™ž๐™ค๐™ฃ! โœจ Many use LLM use cases involve generating outputs with a specific structure. โžก๏ธ For instance when using an LLM as a judge to evaluate another model's outputs, you need it to give you not only a score, but also the rationale for this score, and maybe a confidence level. So you do not need only "score: 1", but more a dictionary like: ``` { "rationale": "The answer does not match the true answer at all." "score": 1, "confidence_level": 0.85 } ``` ๐Ÿค” How to force your LLM to generate such a structured output? ๐Ÿ—๏ธ ๐—–๐—ผ๐—ป๐˜€๐˜๐—ฟ๐—ฎ๐—ถ๐—ป๐—ฒ๐—ฑ ๐—ฑ๐—ฒ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด is a great technique to generate structured output: you can specify a grammar (=set of rules) that the output should follow, and ๐—ฐ๐—ผ๐—ป๐˜€๐˜๐—ฟ๐—ฎ๐—ถ๐—ป๐—ฒ๐—ฑ ๐—ฑ๐—ฒ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด ๐˜๐—ต๐—ฒ๐—ป ๐—ณ๐—ผ๐—ฟ๐—ฐ๐—ฒ๐˜€ ๐˜๐—ต๐—ฒ ๐—ฑ๐—ฒ๐—ฐ๐—ผ๐—ฑ๐—ฒ๐—ฟ ๐˜๐—ผ ๐—ผ๐—ป๐—น๐˜† ๐—ฝ๐—ถ๐—ฐ๐—ธ ๐˜๐—ผ๐—ธ๐—ฒ๐—ป๐˜€ ๐˜๐—ต๐—ฎ๐˜ ๐—ฟ๐—ฒ๐˜€๐—ฝ๐—ฒ๐—ฐ๐˜ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ด๐—ฟ๐—ฎ๐—บ๐—บ๐—ฎ๐—ฟ. I've created a guide to show you how to use it, both via our Inference API and locally using ๐˜ฐ๐˜ถ๐˜ต๐˜ญ๐˜ช๐˜ฏ๐˜ฆ๐˜ด! ๐Ÿ‘‰ Read it here: https://huggingface.co/learn/cookbook/structured_generation Thank you @stevhliu for your great help in improving it!
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2024-05-24T16:06:50.000Z
2024-05-24T16:19:46.958Z
[]
/posts/m-ric/327424604003088
1,037
0
886069136044815
[ { "type": "text", "value": "Hey HuggingFace, love your open source attitude and particularly transformers.js for embedding models! Your current integration \"use this model\" gives you the transformers.js code, but there is no quick way to really test a model in one click. ", "raw": "Hey HuggingFace, love your open source attitude and particularly transformers.js for embedding models! Your current integration \"use this model\" gives you the transformers.js code, but there is no quick way to really test a model in one click. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "SemanticFinder (", "raw": "SemanticFinder (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/do-me/SemanticFinder", "href": null, "resource": { "type": "dataset", "id": "do-me/SemanticFinder", "discussionNum": null }, "url": "https://huggingface.co/datasets/do-me/SemanticFinder", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") offers such an integration for all compatible feature-extraction models! All you need to do is add a URL parameter with the model ID to it, like so: ", "raw": ") offers such an integration for all compatible feature-extraction models! All you need to do is add a URL parameter with the model ID to it, like so: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://do-me.github.io/SemanticFinder/?model=Xenova/bge-small-en-v1.5", "href": "https://do-me.github.io/SemanticFinder/?model=Xenova/bge-small-en-v1.5", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ". You can also decide between quantized and normal mode with ", "raw": ". You can also decide between quantized and normal mode with ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://do-me.github.io/SemanticFinder/?model=Xenova/bge-small-en-v1.5&quantized=false", "href": "https://do-me.github.io/SemanticFinder/?model=Xenova/bge-small-en-v1.5&quantized=false", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ". Maybe that would do for a HF integration?", "raw": ". Maybe that would do for a HF integration?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I know it's a small open source project, but I really believe that it provides value for devs before deciding for one model or the other. Also, it's much easier than having to spin up a notebook, install dependencies etc.. It's private, so you could even do some real-world evaluation on personal data without having to worry about third-party services data policies.", "raw": "I know it's a small open source project, but I really believe that it provides value for devs before deciding for one model or the other. Also, it's much easier than having to spin up a notebook, install dependencies etc.. It's private, so you could even do some real-world evaluation on personal data without having to worry about third-party services data policies.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Happy to hear the community's thoughts! ", "raw": "Happy to hear the community's thoughts! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hey HuggingFace, love your open source attitude and particularly transformers.js for embedding models! Your current integration "use this model" gives you the transformers.js code, but there is no quick way to really test a model in one click. SemanticFinder (https://huggingface.co/datasets/do-me/SemanticFinder) offers such an integration for all compatible feature-extraction models! All you need to do is add a URL parameter with the model ID to it, like so: https://do-me.github.io/SemanticFinder/?model=Xenova/bge-small-en-v1.5. You can also decide between quantized and normal mode with https://do-me.github.io/SemanticFinder/?model=Xenova/bge-small-en-v1.5&quantized=false. Maybe that would do for a HF integration? I know it's a small open source project, but I really believe that it provides value for devs before deciding for one model or the other. Also, it's much easier than having to spin up a notebook, install dependencies etc.. It's private, so you could even do some real-world evaluation on personal data without having to worry about third-party services data policies. Happy to hear the community's thoughts!
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[]
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2024-05-24T12:50:31.000Z
2024-05-24T12:54:12.216Z
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/posts/do-me/886069136044815
1,158
1
370449983890827
[ { "type": "text", "value": "Gloomy AI Image! ๐Ÿ‘€", "raw": "Gloomy AI Image! ๐Ÿ‘€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "(Prompt: Please create me a gloomy forest showing the moon it is a very dark hollow place)", "raw": "(Prompt: Please create me a gloomy forest showing the moon it is a very dark hollow place)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Gloomy AI Image! ๐Ÿ‘€ (Prompt: Please create me a gloomy forest showing the moon it is a very dark hollow place)
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[]
[]
2024-05-24T12:31:36.000Z
2024-05-24T12:31:36.878Z
[]
/posts/BoredApeYachtClub/370449983890827
966
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108070037225246
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The 100 models milestone on the https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard is successfully reached within 10 days after the leaderboard's release ๐Ÿฅณ https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct is still the king of the leaderboard ๐Ÿ‘‘ with a 3.46 points difference compared to its successor https://huggingface.co/CohereForAI/c4ai-command-r-plus who took the 2nd place ๐Ÿฅˆ from his younger brother https://huggingface.co/CohereForAI/c4ai-command-r-v01 that lives today in the 5th floor just behind https://huggingface.co/Ashmal/MBZUAI-oryx -3rd place ๐Ÿฅ‰- (AFAIK an experimental model from MBZUAI) and https://huggingface.co/core42/jais-30b-chat-v3 -4th place- from Core42. PS : I should consider a career in sports commentary ๐Ÿ˜‚ Would you recommend me to BeIN Sports ๐Ÿ˜€ ?
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2024-05-24T10:39:18.000Z
2024-05-24T17:40:32.422Z
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/posts/alielfilali01/108070037225246
1,063
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140773962519700
[ { "type": "text", "value": "๐Ÿš€ Meet MergeUI - an All-in-one UI for Exploring Merged LLMs on Hugging Face ๐Ÿค—! ", "raw": "๐Ÿš€ Meet MergeUI - an All-in-one UI for Exploring Merged LLMs on Hugging Face ๐Ÿค—! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model merging is a cool new technique for creating powerful language models for cheap (no GPU required). But it raises questions like:", "raw": "Model merging is a cool new technique for creating powerful language models for cheap (no GPU required). But it raises questions like:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Which models should we merge?", "raw": "- Which models should we merge?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- What merge strategies work best?", "raw": "- What merge strategies work best?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- How do different base models affect performance?", "raw": "- How do different base models affect performance?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "With MergeUI, you can easily:", "raw": "With MergeUI, you can easily:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Visualise the family tree and lineage of any merged model.", "raw": "- Visualise the family tree and lineage of any merged model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Explore benchmark performance of family trees from the Open LLM Leaderboard.", "raw": "- Explore benchmark performance of family trees from the Open LLM Leaderboard.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Analyse the different merge strategies used.", "raw": "- Analyse the different merge strategies used.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Check license information for merged models and their ancestors.", "raw": "- Check license information for merged models and their ancestors.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "All this helps you explore and understand merged models, uncover valuable insights, and make better decisions for your projects.", "raw": "All this helps you explore and understand merged models, uncover valuable insights, and make better decisions for your projects.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Ready to dive in? Check out these links:", "raw": "Ready to dive in? Check out these links:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- ๐Ÿงฌ Try MergeUI - ", "raw": "- ๐Ÿงฌ Try MergeUI - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://naskio-mergeui.hf.space", "href": "https://naskio-mergeui.hf.space", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- ๐Ÿ‘จโ€๐Ÿ’ป Source Code - ", "raw": "- ๐Ÿ‘จโ€๐Ÿ’ป Source Code - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/naskio/mergeui", "href": "https://github.com/naskio/mergeui", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Love this project? boost it on GitHub and share it with your network.", "raw": "Love this project? boost it on GitHub and share it with your network.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "#merge #mergekit #leaderboard", "raw": "#merge #mergekit #leaderboard", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/naskio/mergeui", "href": null, "resource": { "type": "space", "id": "naskio/mergeui", "discussionNum": null }, "url": "https://huggingface.co/spaces/naskio/mergeui", "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿš€ Meet MergeUI - an All-in-one UI for Exploring Merged LLMs on Hugging Face ๐Ÿค—! Model merging is a cool new technique for creating powerful language models for cheap (no GPU required). But it raises questions like: - Which models should we merge? - What merge strategies work best? - How do different base models affect performance? With MergeUI, you can easily: - Visualise the family tree and lineage of any merged model. - Explore benchmark performance of family trees from the Open LLM Leaderboard. - Analyse the different merge strategies used. - Check license information for merged models and their ancestors. All this helps you explore and understand merged models, uncover valuable insights, and make better decisions for your projects. Ready to dive in? Check out these links: - ๐Ÿงฌ Try MergeUI - https://naskio-mergeui.hf.space - ๐Ÿ‘จโ€๐Ÿ’ป Source Code - https://github.com/naskio/mergeui Love this project? boost it on GitHub and share it with your network. #merge #mergekit #leaderboard https://huggingface.co/spaces/naskio/mergeui
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2024-05-24T06:38:27.000Z
2024-05-24T08:47:25.510Z
[]
/posts/naskio/140773962519700
1,413
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448236825626478
[ { "type": "text", "value": "A non-Instruct LLM assistant is mostly useless. ๐Ÿง", "raw": "A non-Instruct LLM assistant is mostly useless. ๐Ÿง", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Since it's mostly a model trained to complete text, when you ask it a question like \"What to do during a stopover in Paris?\", it can just go on and on adding more details to your question instead of answering, which would be valid to complete text from its training corpus, but not to answer questions.", "raw": "Since it's mostly a model trained to complete text, when you ask it a question like \"What to do during a stopover in Paris?\", it can just go on and on adding more details to your question instead of answering, which would be valid to complete text from its training corpus, but not to answer questions.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžก๏ธ So the post-training stage includes an important Instruction tuning step where you teach your model how to be useful : answer questions, be concise, be polite... RLHF is a well known technique for this.", "raw": "โžก๏ธ So the post-training stage includes an important Instruction tuning step where you teach your model how to be useful : answer questions, be concise, be polite... RLHF is a well known technique for this.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For people interested to understand how this step works, the folks at Adaptive ML have made a great guide!", "raw": "For people interested to understand how this step works, the folks at Adaptive ML have made a great guide!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read it here ๐Ÿ‘‰ ", "raw": "Read it here ๐Ÿ‘‰ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.adaptive-ml.com/post/from-zero-to-ppo", "href": "https://www.adaptive-ml.com/post/from-zero-to-ppo", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
A non-Instruct LLM assistant is mostly useless. ๐Ÿง Since it's mostly a model trained to complete text, when you ask it a question like "What to do during a stopover in Paris?", it can just go on and on adding more details to your question instead of answering, which would be valid to complete text from its training corpus, but not to answer questions. โžก๏ธ So the post-training stage includes an important Instruction tuning step where you teach your model how to be useful : answer questions, be concise, be polite... RLHF is a well known technique for this. For people interested to understand how this step works, the folks at Adaptive ML have made a great guide! Read it here ๐Ÿ‘‰ https://www.adaptive-ml.com/post/from-zero-to-ppo
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2024-11-12T15:58:00.000Z
2024-11-12T15:58:00.491Z
[]
/posts/m-ric/448236825626478
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848636229163970
[ { "type": "text", "value": "Fascinating point from ", "raw": "Fascinating point from ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@thomwolf", "href": null, "resource": null, "url": null, "code": null, "user": "thomwolf", "label": null, "lang": null }, { "type": "text", "value": " at Web Summit: AI misuse (deepfakes, fake news) is actually easier to make with closed models, not with open-source ones.", "raw": " at Web Summit: AI misuse (deepfakes, fake news) is actually easier to make with closed models, not with open-source ones.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This challenges the common narrative that open-source AI is inherently more dangerous. The reality is more nuanced - while we may think open source is technically easier to misuse, closed models' accessibility and product-focused design appear to be driving more actual harm.", "raw": "This challenges the common narrative that open-source AI is inherently more dangerous. The reality is more nuanced - while we may think open source is technically easier to misuse, closed models' accessibility and product-focused design appear to be driving more actual harm.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Important context for current AI safety discussions and regulation debates.", "raw": "Important context for current AI safety discussions and regulation debates.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Do you agree? ๐Ÿ‘‡", "raw": "Do you agree? ๐Ÿ‘‡", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Fascinating point from @thomwolf at Web Summit: AI misuse (deepfakes, fake news) is actually easier to make with closed models, not with open-source ones. This challenges the common narrative that open-source AI is inherently more dangerous. The reality is more nuanced - while we may think open source is technically easier to misuse, closed models' accessibility and product-focused design appear to be driving more actual harm. Important context for current AI safety discussions and regulation debates. Do you agree? ๐Ÿ‘‡
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2024-11-12T15:33:50.000Z
2024-11-13T15:52:40.690Z
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/posts/fdaudens/848636229163970
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Unpopular opinion : o1-preview is more stupid than 4o and Qwen2.5-72B-Instruct in extremely underrated !
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2024-11-12T13:54:40.000Z
2024-11-13T01:27:20.880Z
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/posts/alielfilali01/543270080657899
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https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-demo
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2024-11-12T07:48:59.000Z
2024-11-12T07:48:59.475Z
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/posts/ariG23498/359592864363961
2,526
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[ { "type": "text", "value": "New adapt.s for dev ๐Ÿ”", "raw": "New adapt.s for dev ๐Ÿ”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " Hosted -> ", "raw": " Hosted -> ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC", "href": null, "resource": { "type": "space", "id": "prithivMLmods/FLUX-LoRA-DLC", "discussionNum": null }, "url": "https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC", "code": null, "user": null, "label": null, 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New adapt.s for dev ๐Ÿ” Hosted -> https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC โœจTeen Outfit: https://huggingface.co/prithivMLmods/Teen-Outfit โœจDark Pink: https://huggingface.co/prithivMLmods/Dark-Thing-Flux-LoRA โœจShadow Projection: https://huggingface.co/prithivMLmods/Shadow-Projection-Flux-LoRA โœจAbstract Cartoon: https://huggingface.co/prithivMLmods/Abstract-Cartoon-Flux-LoRA โœจStreet Bokeh: https://huggingface.co/prithivMLmods/Street-Bokeh-Flux-LoRA โœจFine Detailed: https://huggingface.co/prithivMLmods/Flux-Realism-FineDetailed โœจBold Shadows: https://huggingface.co/prithivMLmods/Bold-Shadows-Flux-LoRA โœจYellow Laser: https://huggingface.co/prithivMLmods/Yellow-Laser-Flux-LoRA ------------ ๐ŸŽ‰LoRA Collection: https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be ๐ŸŽ‰LoRA Spaces: https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32 ------------ . . .@prithivMLmods ๐Ÿค—
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2024-11-12T06:00:02.000Z
2024-11-12T06:00:02.737Z
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/posts/prithivMLmods/341606281497201
3,435
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981432338142461
[ { "type": "text", "value": "Sharing a new space to test out ViTPose, a pose estimation model using Visual Transformers.", "raw": "Sharing a new space to test out ViTPose, a pose estimation model using Visual Transformers.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "[ViTPose Playground](", "raw": "[ViTPose Playground](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Dref360/vit_pose_playground", "href": null, "resource": { "type": "space", "id": "Dref360/vit_pose_playground", "discussionNum": null }, "url": "https://huggingface.co/spaces/Dref360/vit_pose_playground", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ")", "raw": ")", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This model will be available in ", "raw": "This model will be available in ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`transformers`", "href": null, "resource": null, "url": null, "code": "transformers", "user": null, "label": null, "lang": null }, { "type": "text", "value": " once [#30530](", "raw": " once [#30530](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/huggingface/transformers/pull/30530", "href": "https://github.com/huggingface/transformers/pull/30530", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") is merged. Huge shoutout to ", "raw": ") is merged. Huge shoutout to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@nielsr", "href": null, "resource": null, "url": null, "code": null, "user": "nielsr", "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@danelcsb", "href": null, "resource": null, "url": null, "code": null, "user": "danelcsb", "label": null, "lang": null }, { "type": "text", "value": " for bringing this to HF!", "raw": " for bringing this to HF!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here's the result on my Ken Halloween costume.", "raw": "Here's the result on my Ken Halloween costume.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Sharing a new space to test out ViTPose, a pose estimation model using Visual Transformers. [ViTPose Playground](https://huggingface.co/spaces/Dref360/vit_pose_playground) This model will be available in `transformers` once [#30530](https://github.com/huggingface/transformers/pull/30530) is merged. Huge shoutout to @nielsr and @danelcsb for bringing this to HF! Here's the result on my Ken Halloween costume.
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2024-11-11T21:55:48.000Z
2024-11-14T11:47:06.194Z
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/posts/Dref360/981432338142461
2,235
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683667017155458
[ { "type": "text", "value": "๐Ÿคฏ AI progress keeps blowing my mind! Just experienced Qwen's new Coder demo - built a complete flashcard web app with a single prompt. The results are incredible!", "raw": "๐Ÿคฏ AI progress keeps blowing my mind! Just experienced Qwen's new Coder demo - built a complete flashcard web app with a single prompt. The results are incredible!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This demo is part of the new Qwen2.5 Coder family (0.5B to 32B models), surpassing/matching GPT4o and Claude Sonnet 3.5 across multiple coding benchmarks.", "raw": "This demo is part of the new Qwen2.5 Coder family (0.5B to 32B models), surpassing/matching GPT4o and Claude Sonnet 3.5 across multiple coding benchmarks.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- 128K context window for 14B/32B models ", "raw": "- 128K context window for 14B/32B models ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Drop-in replacement for GPT-4 in Cursor & Artifacts ", "raw": "- Drop-in replacement for GPT-4 in Cursor & Artifacts ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Models on the Hub under Apache 2.0 license", "raw": "- Models on the Hub under Apache 2.0 license", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”— Try it yourself: ", "raw": "๐Ÿ”— Try it yourself: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts", "href": null, "resource": { "type": "space", "id": "Qwen/Qwen2.5-Coder-Artifacts", "discussionNum": null }, "url": "https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is democratization of coding in real-time. Excited to see AI tools becoming more capable and accessible.", "raw": "This is democratization of coding in real-time. Excited to see AI tools becoming more capable and accessible.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What would you build with this? Share your ideas below! ๐Ÿ‘‡", "raw": "What would you build with this? Share your ideas below! ๐Ÿ‘‡", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "#AI #Programming #TechInnovation #OpenSource #SoftwareDevelopment", "raw": "#AI #Programming #TechInnovation #OpenSource #SoftwareDevelopment", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿคฏ AI progress keeps blowing my mind! Just experienced Qwen's new Coder demo - built a complete flashcard web app with a single prompt. The results are incredible! This demo is part of the new Qwen2.5 Coder family (0.5B to 32B models), surpassing/matching GPT4o and Claude Sonnet 3.5 across multiple coding benchmarks. - 128K context window for 14B/32B models - Drop-in replacement for GPT-4 in Cursor & Artifacts - Models on the Hub under Apache 2.0 license ๐Ÿ”— Try it yourself: https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts This is democratization of coding in real-time. Excited to see AI tools becoming more capable and accessible. What would you build with this? Share your ideas below! ๐Ÿ‘‡ #AI #Programming #TechInnovation #OpenSource #SoftwareDevelopment
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2024-11-11T21:22:17.000Z
2024-11-13T08:06:53.555Z
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/posts/fdaudens/683667017155458
2,226
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[ { "type": "text", "value": "๐ŸŽต Introducing Suno Music Generation Dataset - ", "raw": "๐ŸŽต Introducing Suno Music Generation Dataset - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/nyuuzyou/suno", "href": null, "resource": { "type": "dataset", "id": "nyuuzyou/suno", "discussionNum": null }, "url": "https://huggingface.co/datasets/nyuuzyou/suno", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dataset highlights:", "raw": "Dataset highlights:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- 659,788 AI-generated music samples with comprehensive metadata from suno.com", "raw": "- 659,788 AI-generated music samples with comprehensive metadata from suno.com", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Multilingual content with English as primary language, including Japanese and other languages", "raw": "- Multilingual content with English as primary language, including Japanese and other languages", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Each entry contains rich metadata including:", "raw": "- Each entry contains rich metadata including:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - Unique song ID, audio/video URLs, and thumbnail images", "raw": " - Unique song ID, audio/video URLs, and thumbnail images", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - AI model version and generation parameters", "raw": " - AI model version and generation parameters", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - Song metadata (tags, prompts, duration)", "raw": " - Song metadata (tags, prompts, duration)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - Creator information and engagement metrics", "raw": " - Creator information and engagement metrics", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Released to the public domain under Creative Commons Zero (CC0) license", "raw": "- Released to the public domain under Creative Commons Zero (CC0) license", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The dataset structure includes detailed information about each generated piece, from technical parameters to user engagement metrics, making it particularly valuable for:", "raw": "The dataset structure includes detailed information about each generated piece, from technical parameters to user engagement metrics, making it particularly valuable for:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Music generation model training", "raw": "- Music generation model training", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Cross-modal analysis (text-to-audio relationships)", "raw": "- Cross-modal analysis (text-to-audio relationships)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- User engagement studies", "raw": "- User engagement studies", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Audio classification tasks", "raw": "- Audio classification tasks", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Music style and genre analysis", "raw": "- Music style and genre analysis", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐ŸŽต Introducing Suno Music Generation Dataset - https://huggingface.co/datasets/nyuuzyou/suno Dataset highlights: - 659,788 AI-generated music samples with comprehensive metadata from suno.com - Multilingual content with English as primary language, including Japanese and other languages - Each entry contains rich metadata including: - Unique song ID, audio/video URLs, and thumbnail images - AI model version and generation parameters - Song metadata (tags, prompts, duration) - Creator information and engagement metrics - Released to the public domain under Creative Commons Zero (CC0) license The dataset structure includes detailed information about each generated piece, from technical parameters to user engagement metrics, making it particularly valuable for: - Music generation model training - Cross-modal analysis (text-to-audio relationships) - User engagement studies - Audio classification tasks - Music style and genre analysis
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2024-11-11T19:08:36.000Z
2024-11-11T19:08:36.326Z
[]
/posts/nyuuzyou/391290114296515
2,139
0
657270848213422
[ { "type": "text", "value": "๐—ค๐˜„๐—ฒ๐—ป๐Ÿฎ.๐Ÿฑ-๐—–๐—ผ๐—ฑ๐—ฒ๐—ฟ-๐Ÿฏ๐Ÿฎ๐—•: ๐—ป๐—ฒ๐˜„ ๐—ฏ๐—ฒ๐˜€๐˜-๐—ถ๐—ป-๐—ฐ๐—น๐—ฎ๐˜€๐˜€ ๐—ผ๐—ฝ๐—ฒ๐—ป ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น, ๐—ฏ๐—ฒ๐—ฎ๐˜๐˜€ ๐—š๐—ฃ๐—ง-๐Ÿฐ๐—ผ ๐—ผ๐—ป ๐—บ๐—ผ๐˜€๐˜ ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด ๐—ฏ๐—ฒ๐—ป๐—ฐ๐—ต๐—บ๐—ฎ๐—ฟ๐—ธ๐˜€!๐Ÿ’ฅ", "raw": "๐—ค๐˜„๐—ฒ๐—ป๐Ÿฎ.๐Ÿฑ-๐—–๐—ผ๐—ฑ๐—ฒ๐—ฟ-๐Ÿฏ๐Ÿฎ๐—•: ๐—ป๐—ฒ๐˜„ ๐—ฏ๐—ฒ๐˜€๐˜-๐—ถ๐—ป-๐—ฐ๐—น๐—ฎ๐˜€๐˜€ ๐—ผ๐—ฝ๐—ฒ๐—ป ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น, ๐—ฏ๐—ฒ๐—ฎ๐˜๐˜€ ๐—š๐—ฃ๐—ง-๐Ÿฐ๐—ผ ๐—ผ๐—ป ๐—บ๐—ผ๐˜€๐˜ ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด ๐—ฏ๐—ฒ๐—ป๐—ฐ๐—ต๐—บ๐—ฎ๐—ฟ๐—ธ๐˜€!๐Ÿ’ฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ช It's the first time Open-Source coding model of this size class that clearly matches GPT-4o's coding capabilities!", "raw": "๐Ÿ’ช It's the first time Open-Source coding model of this size class that clearly matches GPT-4o's coding capabilities!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœจ Completes the previous two Qwen 2.5 Coder release with 4 new size: 0.5B, 3B, 14B, 32B", "raw": "โœจ Completes the previous two Qwen 2.5 Coder release with 4 new size: 0.5B, 3B, 14B, 32B", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“š Support long context up to 128K (for the 14B and 32B models)", "raw": "๐Ÿ“š Support long context up to 128K (for the 14B and 32B models)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… Drop-in replacement to GPT-4o as a coding assistant on Cursor or for Artifacts!", "raw": "โœ… Drop-in replacement to GPT-4o as a coding assistant on Cursor or for Artifacts!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค— Models available right now on the Hub, under Apache 2.0 license!", "raw": "๐Ÿค— Models available right now on the Hub, under Apache 2.0 license!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "They have setup a crazy Artifacts demo, you should go have a look!", "raw": "They have setup a crazy Artifacts demo, you should go have a look!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ ", "raw": "๐Ÿ‘‰ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts", "href": null, "resource": { "type": "space", "id": "Qwen/Qwen2.5-Coder-Artifacts", "discussionNum": null }, "url": "https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts", "code": null, "user": null, "label": null, "lang": null } ]
๐—ค๐˜„๐—ฒ๐—ป๐Ÿฎ.๐Ÿฑ-๐—–๐—ผ๐—ฑ๐—ฒ๐—ฟ-๐Ÿฏ๐Ÿฎ๐—•: ๐—ป๐—ฒ๐˜„ ๐—ฏ๐—ฒ๐˜€๐˜-๐—ถ๐—ป-๐—ฐ๐—น๐—ฎ๐˜€๐˜€ ๐—ผ๐—ฝ๐—ฒ๐—ป ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น, ๐—ฏ๐—ฒ๐—ฎ๐˜๐˜€ ๐—š๐—ฃ๐—ง-๐Ÿฐ๐—ผ ๐—ผ๐—ป ๐—บ๐—ผ๐˜€๐˜ ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด ๐—ฏ๐—ฒ๐—ป๐—ฐ๐—ต๐—บ๐—ฎ๐—ฟ๐—ธ๐˜€!๐Ÿ’ฅ ๐Ÿ’ช It's the first time Open-Source coding model of this size class that clearly matches GPT-4o's coding capabilities! โœจ Completes the previous two Qwen 2.5 Coder release with 4 new size: 0.5B, 3B, 14B, 32B ๐Ÿ“š Support long context up to 128K (for the 14B and 32B models) โœ… Drop-in replacement to GPT-4o as a coding assistant on Cursor or for Artifacts! ๐Ÿค— Models available right now on the Hub, under Apache 2.0 license! They have setup a crazy Artifacts demo, you should go have a look! ๐Ÿ‘‰ https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts
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2024-11-11T18:33:40.000Z
2024-11-11T18:33:40.664Z
[]
/posts/m-ric/657270848213422
3,150
0
254681486405618
[ { "type": "text", "value": "๐—”๐—ฟ๐—ฒ ๐˜€๐—ฐ๐—ฎ๐—น๐—ถ๐—ป๐—ด ๐—น๐—ฎ๐˜„๐˜€ ๐—ผ๐˜ƒ๐—ฒ๐—ฟ? ๐—” ๐—ฟ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜ ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜๐—ต๐—ฒ ๐—œ๐—ป๐—ณ๐—ผ๐—ฟ๐—บ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฎ๐—ป๐—ป๐—ผ๐˜‚๐—ป๐—ฐ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฎ๐˜ ๐—ข๐—ฝ๐—ฒ๐—ป๐—”๐—œ ๐—ถ๐˜€ ๐˜€๐—ฒ๐—ฒ๐—ถ๐—ป๐—ด ๐—ฑ๐—ถ๐—บ๐—ถ๐—ป๐—ถ๐˜€๐—ต๐—ถ๐—ป๐—ด ๐—ฟ๐—ฒ๐˜๐˜‚๐—ฟ๐—ป๐˜€ ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜€๐—ฐ๐—ฎ๐—น๐—ถ๐—ป๐—ด ๐˜‚๐—ฝ ๐˜๐—ต๐—ฒ ๐—ป๐—ฒ๐˜…๐˜ ๐—š๐—ฃ๐—ง ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€.", "raw": "๐—”๐—ฟ๐—ฒ ๐˜€๐—ฐ๐—ฎ๐—น๐—ถ๐—ป๐—ด ๐—น๐—ฎ๐˜„๐˜€ ๐—ผ๐˜ƒ๐—ฒ๐—ฟ? ๐—” ๐—ฟ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜ ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜๐—ต๐—ฒ ๐—œ๐—ป๐—ณ๐—ผ๐—ฟ๐—บ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฎ๐—ป๐—ป๐—ผ๐˜‚๐—ป๐—ฐ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฎ๐˜ ๐—ข๐—ฝ๐—ฒ๐—ป๐—”๐—œ ๐—ถ๐˜€ ๐˜€๐—ฒ๐—ฒ๐—ถ๐—ป๐—ด ๐—ฑ๐—ถ๐—บ๐—ถ๐—ป๐—ถ๐˜€๐—ต๐—ถ๐—ป๐—ด ๐—ฟ๐—ฒ๐˜๐˜‚๐—ฟ๐—ป๐˜€ ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜€๐—ฐ๐—ฎ๐—น๐—ถ๐—ป๐—ด ๐˜‚๐—ฝ ๐˜๐—ต๐—ฒ ๐—ป๐—ฒ๐˜…๐˜ ๐—š๐—ฃ๐—ง ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“Š What are scaling laws? These are empiric laws that say \"Every time you increase compute spent in training 10-fold, your LLM's performance will go up by a predictable tick\". Of course, they apply only if you train your model with the right methods.", "raw": "๐Ÿ“Š What are scaling laws? These are empiric laws that say \"Every time you increase compute spent in training 10-fold, your LLM's performance will go up by a predictable tick\". Of course, they apply only if you train your model with the right methods.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The image below illustrates it: they're from a paper by Google, \"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation\", and they show how quality and instruction following of models improve when you scale the model up (which is equivalent to scaling up the compute spent in training).", "raw": "The image below illustrates it: they're from a paper by Google, \"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation\", and they show how quality and instruction following of models improve when you scale the model up (which is equivalent to scaling up the compute spent in training).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžก๏ธ These scaling laws have immense impact: they triggered the largest gold rush ever, with companies pouring billions into scaling up theiur training. Microsoft and OpenAI spent 100B into their \"Startgate\" mega training cluster, due to start running in 2028.", "raw": "โžก๏ธ These scaling laws have immense impact: they triggered the largest gold rush ever, with companies pouring billions into scaling up theiur training. Microsoft and OpenAI spent 100B into their \"Startgate\" mega training cluster, due to start running in 2028.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค” So, what about these reports of scaling laws slowing down?", "raw": "๐Ÿค” So, what about these reports of scaling laws slowing down?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If they are true, they would mean a gigantic paradigm shift, as the hundreds of billions poured by AI companies into scaling could be a dead-end. โ›”๏ธ", "raw": "If they are true, they would mean a gigantic paradigm shift, as the hundreds of billions poured by AI companies into scaling could be a dead-end. โ›”๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "But I doubt it: until the most recent publications, scaling laws showed no signs of weakness, and the researchers at the higher end of the scale-up seems to imply the scaling up continues. ", "raw": "But I doubt it: until the most recent publications, scaling laws showed no signs of weakness, and the researchers at the higher end of the scale-up seems to imply the scaling up continues. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Wait and see!", "raw": "Wait and see!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐—”๐—ฟ๐—ฒ ๐˜€๐—ฐ๐—ฎ๐—น๐—ถ๐—ป๐—ด ๐—น๐—ฎ๐˜„๐˜€ ๐—ผ๐˜ƒ๐—ฒ๐—ฟ? ๐—” ๐—ฟ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜ ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜๐—ต๐—ฒ ๐—œ๐—ป๐—ณ๐—ผ๐—ฟ๐—บ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฎ๐—ป๐—ป๐—ผ๐˜‚๐—ป๐—ฐ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฎ๐˜ ๐—ข๐—ฝ๐—ฒ๐—ป๐—”๐—œ ๐—ถ๐˜€ ๐˜€๐—ฒ๐—ฒ๐—ถ๐—ป๐—ด ๐—ฑ๐—ถ๐—บ๐—ถ๐—ป๐—ถ๐˜€๐—ต๐—ถ๐—ป๐—ด ๐—ฟ๐—ฒ๐˜๐˜‚๐—ฟ๐—ป๐˜€ ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜€๐—ฐ๐—ฎ๐—น๐—ถ๐—ป๐—ด ๐˜‚๐—ฝ ๐˜๐—ต๐—ฒ ๐—ป๐—ฒ๐˜…๐˜ ๐—š๐—ฃ๐—ง ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€. ๐Ÿ“Š What are scaling laws? These are empiric laws that say "Every time you increase compute spent in training 10-fold, your LLM's performance will go up by a predictable tick". Of course, they apply only if you train your model with the right methods. The image below illustrates it: they're from a paper by Google, "Scaling Autoregressive Models for Content-Rich Text-to-Image Generation", and they show how quality and instruction following of models improve when you scale the model up (which is equivalent to scaling up the compute spent in training). โžก๏ธ These scaling laws have immense impact: they triggered the largest gold rush ever, with companies pouring billions into scaling up theiur training. Microsoft and OpenAI spent 100B into their "Startgate" mega training cluster, due to start running in 2028. ๐Ÿค” So, what about these reports of scaling laws slowing down? If they are true, they would mean a gigantic paradigm shift, as the hundreds of billions poured by AI companies into scaling could be a dead-end. โ›”๏ธ But I doubt it: until the most recent publications, scaling laws showed no signs of weakness, and the researchers at the higher end of the scale-up seems to imply the scaling up continues. Wait and see!
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2024-11-11T17:51:50.000Z
2024-11-13T08:35:45.216Z
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/posts/m-ric/254681486405618
776
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490293792676401
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New preprint out with colleagues from MIT and IBM Research https://huggingface.co/papers/2405.12981 We introduce a simple mechanism of sharing keys and values across layers, reducing the memory needed for KV cache during inference!!
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2024-05-24T03:03:33.000Z
2024-05-24T09:02:01.295Z
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/posts/mayank-mishra/490293792676401
1,741
1
423954012888625
[ { "type": "text", "value": "the musicgen max4live device", "raw": "the musicgen max4live device", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "gary4live", "raw": "gary4live", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "now has a popup UI so you can minimize ableton and have it keep trying/retrying from the input audio you recorded in the DAW", "raw": "now has a popup UI so you can minimize ableton and have it keep trying/retrying from the input audio you recorded in the DAW", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "while coding lol", "raw": "while coding lol", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
the musicgen max4live device gary4live now has a popup UI so you can minimize ableton and have it keep trying/retrying from the input audio you recorded in the DAW while coding lol
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[]
[]
2024-05-23T23:49:22.000Z
2024-05-23T23:49:22.935Z
[]
/posts/thecollabagepatch/423954012888625
1,374
0
549145567783881
[ { "type": "text", "value": "Aiming to keep ", "raw": "Aiming to keep ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`timm`", "href": null, "resource": null, "url": null, "code": "timm", "user": null, "label": null, "lang": null }, { "type": "text", "value": " (", "raw": " (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/huggingface/pytorch-image-models", "href": "https://github.com/huggingface/pytorch-image-models", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") the go-to library for efficient image encoders for your mobile and edge devices, I've started working on an implementation of the new MobileNet-V4 model. Take a look at a short article I wrote about the model: ", "raw": ") the go-to library for efficient image encoders for your mobile and edge devices, I've started working on an implementation of the new MobileNet-V4 model. Take a look at a short article I wrote about the model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/rwightman/mobilenetv4", "href": "https://huggingface.co/blog/rwightman/mobilenetv4", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2404.10518", "href": null, "resource": { "type": "paper", "id": "2404.10518", "discussionNum": null }, "url": "https://huggingface.co/papers/2404.10518", "code": null, "user": null, "label": "MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)", "lang": null } ]
Aiming to keep `timm` (https://github.com/huggingface/pytorch-image-models) the go-to library for efficient image encoders for your mobile and edge devices, I've started working on an implementation of the new MobileNet-V4 model. Take a look at a short article I wrote about the model: https://huggingface.co/blog/rwightman/mobilenetv4 Paper: https://huggingface.co/papers/2404.10518
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2024-05-23T23:48:47.000Z
2024-05-28T19:02:28.223Z
[]
/posts/rwightman/549145567783881
1,485
0
493712746905116
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mlx port of karpathyโ€™s minbpe ๐Ÿค• Minimal (byte-level) Byte Pair Encoding tokenizer. Algorithmically follows along the GPT2 tokenizer. Code: https://github.com/Jaykef/mlx-minbpe
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2024-05-23T23:21:23.000Z
2024-05-23T23:27:22.484Z
[]
/posts/Jaward/493712746905116
993
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[ { "type": "text", "value": "Introducing Kraken-LoRA โ€“ a lightweight version of Kraken that uses LoRA-Adapters as Experts based on the base model.", "raw": "Introducing Kraken-LoRA โ€“ a lightweight version of Kraken that uses LoRA-Adapters as Experts based on the base model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@fernandofernandes", "href": null, "resource": null, "url": null, "code": null, "user": "fernandofernandes", "label": null, "lang": null }, { "type": "text", "value": " , me, ", "raw": " , me, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Crystalcareai", "href": null, "resource": null, "url": null, "code": null, "user": "Crystalcareai", "label": null, "lang": null }, { "type": "text", "value": " , ", "raw": " , ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@ehartford", "href": null, "resource": null, "url": null, "code": null, "user": "ehartford", "label": null, "lang": null }, { "type": "text", "value": " created the Kraken-LoRA!", "raw": " created the Kraken-LoRA!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ” Whatโ€™s the big deal?", "raw": "๐Ÿ” Whatโ€™s the big deal?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… Size Consistency: While Krakenโ€™s size increases with more Experts, Kraken-LoRA remains as compact as the base model (e.g., 8b if you use Meta-Llama3-8b-Instruct).", "raw": "โœ… Size Consistency: While Krakenโ€™s size increases with more Experts, Kraken-LoRA remains as compact as the base model (e.g., 8b if you use Meta-Llama3-8b-Instruct).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… VRAM Efficiency: Kraken-LoRA is highly VRAM efficient, maintaining the power of all experts without the bloat.", "raw": "โœ… VRAM Efficiency: Kraken-LoRA is highly VRAM efficient, maintaining the power of all experts without the bloat.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… Dynamic Adaptation: LoRA adapters are applied dynamically at runtime, following the routing process.", "raw": "โœ… Dynamic Adaptation: LoRA adapters are applied dynamically at runtime, following the routing process.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… High Efficiency: Enjoy increased efficiency without compromising performance, as long as the LoRA adapters match the base model.", "raw": "โœ… High Efficiency: Enjoy increased efficiency without compromising performance, as long as the LoRA adapters match the base model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ก Conclusion: Kraken-LoRA empowers businesses to experience enhanced flexibility and performance from our architecture, enabling further scalability without sacrificing performance.", "raw": "๐Ÿ’ก Conclusion: Kraken-LoRA empowers businesses to experience enhanced flexibility and performance from our architecture, enabling further scalability without sacrificing performance.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check out the model here: ", "raw": "Check out the model here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/VAGOsolutions/Kraken-LoRA", "href": null, "resource": { "type": "model", "id": "VAGOsolutions/Kraken-LoRA", "discussionNum": null }, "url": "https://huggingface.co/VAGOsolutions/Kraken-LoRA", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Explore the code here: ", "raw": "Explore the code here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/cognitivecomputations/kraken/tree/main/Kraken-LoRA", "href": "https://github.com/cognitivecomputations/kraken/tree/main/Kraken-LoRA", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Have fun with Kraken-LoRA! ๐Ÿ™", "raw": "Have fun with Kraken-LoRA! ๐Ÿ™", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Introducing Kraken-LoRA โ€“ a lightweight version of Kraken that uses LoRA-Adapters as Experts based on the base model. @fernandofernandes , me, @Crystalcareai , @ehartford created the Kraken-LoRA! ๐Ÿ” Whatโ€™s the big deal? โœ… Size Consistency: While Krakenโ€™s size increases with more Experts, Kraken-LoRA remains as compact as the base model (e.g., 8b if you use Meta-Llama3-8b-Instruct). โœ… VRAM Efficiency: Kraken-LoRA is highly VRAM efficient, maintaining the power of all experts without the bloat. โœ… Dynamic Adaptation: LoRA adapters are applied dynamically at runtime, following the routing process. โœ… High Efficiency: Enjoy increased efficiency without compromising performance, as long as the LoRA adapters match the base model. ๐Ÿ’ก Conclusion: Kraken-LoRA empowers businesses to experience enhanced flexibility and performance from our architecture, enabling further scalability without sacrificing performance. Check out the model here: https://huggingface.co/VAGOsolutions/Kraken-LoRA Explore the code here: https://github.com/cognitivecomputations/kraken/tree/main/Kraken-LoRA Have fun with Kraken-LoRA! ๐Ÿ™
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2024-05-23T20:01:49.000Z
2024-05-23T20:01:49.546Z
[]
/posts/DavidGF/503105573610507
1,441
0
701653010948323
[ { "type": "text", "value": "Switching from French to German to Chinese in the same discussion ๐Ÿ˜…", "raw": "Switching from French to German to Chinese in the same discussion ๐Ÿ˜…", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Impressive to see Cohere for AI's new Aya model multilingual capabilities.", "raw": "Impressive to see Cohere for AI's new Aya model multilingual capabilities.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- C4AI Aya 23 is a research open weights release", "raw": "- C4AI Aya 23 is a research open weights release", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- 8 and 35 billion parameter models", "raw": "- 8 and 35 billion parameter models", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- 23 languages supported", "raw": "- 23 languages supported", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can try it out here: ", "raw": "You can try it out here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/CohereForAI/aya-23", "href": "https://huggingface.co/spaces/CohereForAI/aya-23", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Switching from French to German to Chinese in the same discussion ๐Ÿ˜… Impressive to see Cohere for AI's new Aya model multilingual capabilities. - C4AI Aya 23 is a research open weights release - 8 and 35 billion parameter models - 23 languages supported You can try it out here: https://huggingface.co/spaces/CohereForAI/aya-23
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2024-05-23T18:31:22.000Z
2024-05-24T09:02:29.610Z
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/posts/fdaudens/701653010948323
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[ { "type": "text", "value": "Cohere's Aya 8B & 35B ๐Ÿ”ฅ", "raw": "Cohere's Aya 8B & 35B ๐Ÿ”ฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "> Multilingual (23 languages), beats Mistral 7B and Llama3 8B in preferenceโ€”open weights.", "raw": "> Multilingual (23 languages), beats Mistral 7B and Llama3 8B in preferenceโ€”open weights.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " capabilities:", "raw": " capabilities:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŒ **Multilingual Mastery**: Supporting 23 languages, including Arabic! ", "raw": "๐ŸŒ **Multilingual Mastery**: Supporting 23 languages, including Arabic! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ† **Top Performer**: Outperforms Mistral 7B and Llama3 8B in user preference.", "raw": "๐Ÿ† **Top Performer**: Outperforms Mistral 7B and Llama3 8B in user preference.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ” **Open Weights**: Access open weights for your research and projects.", "raw": "๐Ÿ” **Open Weights**: Access open weights for your research and projects.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”— **License**: CC-BY-NC with adherence to C4AI's Acceptable Use Policy.", "raw": "๐Ÿ”— **License**: CC-BY-NC with adherence to C4AI's Acceptable Use Policy.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ผ **Developed by**: Cohere For AI and Cohere.", "raw": "๐Ÿ’ผ **Developed by**: Cohere For AI and Cohere.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check out Aya 23 on Hugging Face , link is in comments", "raw": "Check out Aya 23 on Hugging Face , link is in comments", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "#AI #MachineLearning #NLP #Multilingual #Arabic #TechInnovation #OpenSource #CohereAI #AyaModel", "raw": "#AI #MachineLearning #NLP #Multilingual #Arabic #TechInnovation #OpenSource #CohereAI #AyaModel", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Cohere's Aya 8B & 35B ๐Ÿ”ฅ > Multilingual (23 languages), beats Mistral 7B and Llama3 8B in preferenceโ€”open weights. capabilities: ๐ŸŒ **Multilingual Mastery**: Supporting 23 languages, including Arabic! ๐Ÿ† **Top Performer**: Outperforms Mistral 7B and Llama3 8B in user preference. ๐Ÿ” **Open Weights**: Access open weights for your research and projects. ๐Ÿ”— **License**: CC-BY-NC with adherence to C4AI's Acceptable Use Policy. ๐Ÿ’ผ **Developed by**: Cohere For AI and Cohere. Check out Aya 23 on Hugging Face , link is in comments #AI #MachineLearning #NLP #Multilingual #Arabic #TechInnovation #OpenSource #CohereAI #AyaModel
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2024-05-23T17:10:21.000Z
2024-06-20T13:45:27.964Z
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/posts/Salama1429/363436102246984
1,296
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[ { "type": "text", "value": "Good folks at ", "raw": "Good folks at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Meta", "href": null, "resource": null, "url": null, "code": null, "user": "Meta", "label": null, "lang": null }, { "type": "text", "value": " have introduced Chameleon ๐ŸฆŽ (who names these things? ๐Ÿคทโ€โ™‚๏ธ)", "raw": " have introduced Chameleon ๐ŸฆŽ (who names these things? ๐Ÿคทโ€โ™‚๏ธ)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Chameleon is an AI model that can work with multiple types of data, like text and images, all at once. ๐Ÿ–ผ๏ธ๐Ÿ“", "raw": "Chameleon is an AI model that can work with multiple types of data, like text and images, all at once. ๐Ÿ–ผ๏ธ๐Ÿ“", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Before you start searching, as of this post, the model/code have not been open-sourced nor is there any commitment to open-source... sorry! ๐Ÿšซ๐Ÿ”“", "raw": "Before you start searching, as of this post, the model/code have not been open-sourced nor is there any commitment to open-source... sorry! ๐Ÿšซ๐Ÿ”“", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Still, here is the technical stuff:", "raw": "Still, here is the technical stuff:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ Challenges with Current Systems:", "raw": "๐Ÿ‘‰ Challenges with Current Systems:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“‰ Fragmentation: Current multimodal models are often specialized for either text or image tasks, lacking unified approaches.", "raw": "๐Ÿ“‰ Fragmentation: Current multimodal models are often specialized for either text or image tasks, lacking unified approaches.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“Š Scalability: Existing systems struggle with scaling to handle complex, mixed-modal tasks without significant performance degradation.", "raw": "๐Ÿ“Š Scalability: Existing systems struggle with scaling to handle complex, mixed-modal tasks without significant performance degradation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”„ Alignment: Aligning textual and visual modalities remains a technical challenge, often requiring separate processing pipelines.", "raw": "๐Ÿ”„ Alignment: Aligning textual and visual modalities remains a technical challenge, often requiring separate processing pipelines.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ Objective:", "raw": "๐Ÿ‘‰ Objective:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŽฏ Unified Modeling: Develop a single model capable of handling various multimodal tasks (text generation, image generation, image captioning, visual question answering) seamlessly.", "raw": "๐ŸŽฏ Unified Modeling: Develop a single model capable of handling various multimodal tasks (text generation, image generation, image captioning, visual question answering) seamlessly.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ How It's Done ๐Ÿ“˜", "raw": "๐Ÿ‘‰ How It's Done ๐Ÿ“˜", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Early-Fusion Architecture ๐Ÿง : Utilizes an early-fusion token-based approach to integrate text and image data from the beginning.", "raw": "Early-Fusion Architecture ๐Ÿง : Utilizes an early-fusion token-based approach to integrate text and image data from the beginning.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Stable Training ๐Ÿ’ช: Implements a tailored alignment recipe and specific architectural parameterization to ensure stability in mixed-modal settings.", "raw": "Stable Training ๐Ÿ’ช: Implements a tailored alignment recipe and specific architectural parameterization to ensure stability in mixed-modal settings.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Broad Evaluation ๐Ÿ“Š: Assesses the model across various tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed-modal generation.", "raw": "Broad Evaluation ๐Ÿ“Š: Assesses the model across various tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed-modal generation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ Results: (Fun fact they mention Llava-1.5 in comparison but never really share the results)", "raw": "๐Ÿ‘‰ Results: (Fun fact they mention Llava-1.5 in comparison but never really share the results)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ† Performance: Chameleon achieves state-of-the-art results in image captioning and outperforms models like Llama-2 in text-only tasks.", "raw": "๐Ÿ† Performance: Chameleon achieves state-of-the-art results in image captioning and outperforms models like Llama-2 in text-only tasks.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โš–๏ธ Competitiveness: It shows competitive performance with models such as Mixtral 8x7B and Gemini-Pro.", "raw": "โš–๏ธ Competitiveness: It shows competitive performance with models such as Mixtral 8x7B and Gemini-Pro.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘ฉโ€โš–๏ธ Human Judgments: Matches or exceeds the performance of larger models, including Gemini Pro and GPT-4V", "raw": "๐Ÿ‘ฉโ€โš–๏ธ Human Judgments: Matches or exceeds the performance of larger models, including Gemini Pro and GPT-4V", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2405.09818", "href": null, "resource": { "type": "paper", "id": "2405.09818", "discussionNum": null }, "url": "https://huggingface.co/papers/2405.09818", "code": null, "user": null, "label": "Chameleon: Mixed-Modal Early-Fusion Foundation Models (2405.09818)", "lang": null } ]
Good folks at @Meta have introduced Chameleon ๐ŸฆŽ (who names these things? ๐Ÿคทโ€โ™‚๏ธ) Chameleon is an AI model that can work with multiple types of data, like text and images, all at once. ๐Ÿ–ผ๏ธ๐Ÿ“ Before you start searching, as of this post, the model/code have not been open-sourced nor is there any commitment to open-source... sorry! ๐Ÿšซ๐Ÿ”“ Still, here is the technical stuff: ๐Ÿ‘‰ Challenges with Current Systems: ๐Ÿ“‰ Fragmentation: Current multimodal models are often specialized for either text or image tasks, lacking unified approaches. ๐Ÿ“Š Scalability: Existing systems struggle with scaling to handle complex, mixed-modal tasks without significant performance degradation. ๐Ÿ”„ Alignment: Aligning textual and visual modalities remains a technical challenge, often requiring separate processing pipelines. ๐Ÿ‘‰ Objective: ๐ŸŽฏ Unified Modeling: Develop a single model capable of handling various multimodal tasks (text generation, image generation, image captioning, visual question answering) seamlessly. ๐Ÿ‘‰ How It's Done ๐Ÿ“˜ Early-Fusion Architecture ๐Ÿง : Utilizes an early-fusion token-based approach to integrate text and image data from the beginning. Stable Training ๐Ÿ’ช: Implements a tailored alignment recipe and specific architectural parameterization to ensure stability in mixed-modal settings. Broad Evaluation ๐Ÿ“Š: Assesses the model across various tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed-modal generation. ๐Ÿ‘‰ Results: (Fun fact they mention Llava-1.5 in comparison but never really share the results) ๐Ÿ† Performance: Chameleon achieves state-of-the-art results in image captioning and outperforms models like Llama-2 in text-only tasks. โš–๏ธ Competitiveness: It shows competitive performance with models such as Mixtral 8x7B and Gemini-Pro. ๐Ÿ‘ฉโ€โš–๏ธ Human Judgments: Matches or exceeds the performance of larger models, including Gemini Pro and GPT-4V Paper: https://huggingface.co/papers/2405.09818
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2024-05-23T12:34:08.000Z
2024-05-23T13:31:48.461Z
[]
/posts/singhsidhukuldeep/807928006985763
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[ { "type": "text", "value": "How can we use open LLMs to create data for training sentence similarity models? ", "raw": "How can we use open LLMs to create data for training sentence similarity models? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "One of the most exciting use cases for LLMs is generating synthetic datasets that can be used to train non-LLM models. In the past, gathering enough data was one of the most significant barriers to training task-specific models. LLMs can potentially help in this area. ", "raw": "One of the most exciting use cases for LLMs is generating synthetic datasets that can be used to train non-LLM models. In the past, gathering enough data was one of the most significant barriers to training task-specific models. LLMs can potentially help in this area. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I've just written a new blog post on using ", "raw": "I've just written a new blog post on using ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct", "href": null, "resource": { "type": "model", "id": "meta-llama/Meta-Llama-3-70B-Instruct", "discussionNum": null }, "url": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " to generate synthetic similarity data based on the approach from ", "raw": " to generate synthetic similarity data based on the approach from ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2305.12517", "href": null, "resource": { "type": "paper", "id": "2305.12517", "discussionNum": null }, "url": "https://huggingface.co/papers/2305.12517", "code": null, "user": null, "label": "Retrieving Texts based on Abstract Descriptions (2305.12517)", "lang": null }, { "type": "text", "value": ". ", "raw": ". ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/davanstrien/synthetic-similarity-datasets", "href": "https://huggingface.co/blog/davanstrien/synthetic-similarity-datasets", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
How can we use open LLMs to create data for training sentence similarity models? One of the most exciting use cases for LLMs is generating synthetic datasets that can be used to train non-LLM models. In the past, gathering enough data was one of the most significant barriers to training task-specific models. LLMs can potentially help in this area. I've just written a new blog post on using https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct to generate synthetic similarity data based on the approach from https://huggingface.co/papers/2305.12517. https://huggingface.co/blog/davanstrien/synthetic-similarity-datasets
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2024-05-23T08:54:18.000Z
2024-05-23T08:54:36.956Z
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[ { "type": "text", "value": "๐Ÿš€๐ŸŽญ๐ŸŒŸ New Research Alert - Gaussian Head & Shoulders (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿš€", "raw": "๐Ÿš€๐ŸŽญ๐ŸŒŸ New Research Alert - Gaussian Head & Shoulders (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“„ Title: Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with Anchor Gaussian Guided Texture Warping ๐Ÿ”", "raw": "๐Ÿ“„ Title: Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with Anchor Gaussian Guided Texture Warping ๐Ÿ”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“ Description: Gaussian Head & Shoulders is a method for creating high-fidelity upper body avatars by integrating 3D morphable head models with a neural texture warping approach to overcome the limitations of Gaussian splatting.", "raw": "๐Ÿ“ Description: Gaussian Head & Shoulders is a method for creating high-fidelity upper body avatars by integrating 3D morphable head models with a neural texture warping approach to overcome the limitations of Gaussian splatting.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘ฅ Authors: Tianhao Wu et al.", "raw": "๐Ÿ‘ฅ Authors: Tianhao Wu et al.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“„ Paper: ", "raw": "๐Ÿ“„ Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2405.12069", "href": null, "resource": { "type": "paper", "id": "2405.12069", "discussionNum": null }, "url": "https://huggingface.co/papers/2405.12069", "code": null, "user": null, "label": "Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with\n Anchor Gaussian Guided Texture Warping (2405.12069)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŒ Github Page: ", "raw": "๐ŸŒ Github Page: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://gaussian-head-shoulders.netlify.app", "href": "https://gaussian-head-shoulders.netlify.app", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "raw": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "href": null, "resource": { "type": "space", "id": "DmitryRyumin/NewEraAI-Papers", "discussionNum": null }, "url": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " curated by ", "raw": " curated by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@DmitryRyumin", "href": null, "resource": null, "url": null, "code": null, "user": "DmitryRyumin", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ Added to the Avatars Collection: ", "raw": "๐Ÿš€ Added to the Avatars Collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "href": null, "resource": { "type": "collection", "id": "DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "discussionNum": null }, "url": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ” Keywords: #3DModeling #NeuralAvatars #GaussianSplatting #HighFidelityAvatars #3DReconstruction #AvatarRendering #TextureWarping #ComputerGraphics #DeepLearning #ComputerVision #Innovation", "raw": "๐Ÿ” Keywords: #3DModeling #NeuralAvatars #GaussianSplatting #HighFidelityAvatars #3DReconstruction #AvatarRendering #TextureWarping #ComputerGraphics #DeepLearning #ComputerVision #Innovation", "href": null, "resource": null, "url": 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๐Ÿš€๐ŸŽญ๐ŸŒŸ New Research Alert - Gaussian Head & Shoulders (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿš€ ๐Ÿ“„ Title: Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with Anchor Gaussian Guided Texture Warping ๐Ÿ” ๐Ÿ“ Description: Gaussian Head & Shoulders is a method for creating high-fidelity upper body avatars by integrating 3D morphable head models with a neural texture warping approach to overcome the limitations of Gaussian splatting. ๐Ÿ‘ฅ Authors: Tianhao Wu et al. ๐Ÿ“„ Paper: https://huggingface.co/papers/2405.12069 ๐ŸŒ Github Page: https://gaussian-head-shoulders.netlify.app ๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin ๐Ÿš€ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36 ๐Ÿ” Keywords: #3DModeling #NeuralAvatars #GaussianSplatting #HighFidelityAvatars #3DReconstruction #AvatarRendering #TextureWarping #ComputerGraphics #DeepLearning #ComputerVision #Innovation
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2024-05-23T07:56:17.000Z
2024-05-23T07:56:17.986Z
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[ { "type": "text", "value": "We are hiring a \"Developer Experience Engineer for Inference\" at Hugging Face! If you want to make it easier for millions of people to use modern machine learning inference, apply! You can either work from one of our offices e.g. in Paris or New York, or work fully remotely. Details: ", "raw": "We are hiring a \"Developer Experience Engineer for Inference\" at Hugging Face! If you want to make it easier for millions of people to use modern machine learning inference, apply! You can either work from one of our offices e.g. in Paris or New York, or work fully remotely. Details: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://apply.workable.com/huggingface/j/E732F4B8FC/", "href": "https://apply.workable.com/huggingface/j/E732F4B8FC/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
We are hiring a "Developer Experience Engineer for Inference" at Hugging Face! If you want to make it easier for millions of people to use modern machine learning inference, apply! You can either work from one of our offices e.g. in Paris or New York, or work fully remotely. Details: https://apply.workable.com/huggingface/j/E732F4B8FC/
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2024-05-23T07:21:08.000Z
2024-05-23T07:21:08.235Z
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[ { "type": "text", "value": "The Google Deep Mind Team just released a new technical report on Gemini 1.5 Pro and Gemini 1.5 Flash. ", "raw": "The Google Deep Mind Team just released a new technical report on Gemini 1.5 Pro and Gemini 1.5 Flash. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "in addition to architecture, benchmark and evaluation details, the report also provides a few real world use cases for the models such as professional task optimization and translation of lesser-known languages.", "raw": "in addition to architecture, benchmark and evaluation details, the report also provides a few real world use cases for the models such as professional task optimization and translation of lesser-known languages.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can check out the full report here: ", "raw": "You can check out the full report here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://storage.googleapis.com/deepmind-media/gemini/gemini_v1_5_report.pdf?utm_source=substack&utm_medium=email", "href": "https://storage.googleapis.com/deepmind-media/gemini/gemini_v1_5_report.pdf?utm_source=substack&utm_medium=email", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
The Google Deep Mind Team just released a new technical report on Gemini 1.5 Pro and Gemini 1.5 Flash. in addition to architecture, benchmark and evaluation details, the report also provides a few real world use cases for the models such as professional task optimization and translation of lesser-known languages. You can check out the full report here: https://storage.googleapis.com/deepmind-media/gemini/gemini_v1_5_report.pdf?utm_source=substack&utm_medium=email
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2024-05-23T06:48:13.000Z
2024-05-24T03:19:13.052Z
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[ { "type": "text", "value": "Thanks to ", "raw": "Thanks to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@OzzyGT", "href": null, "resource": null, "url": null, "code": null, "user": "OzzyGT", "label": null, "lang": null }, { "type": "text", "value": " for pushing the new Anyline preprocessor to ", "raw": " for pushing the new Anyline preprocessor to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/huggingface/controlnet_aux", "href": "https://github.com/huggingface/controlnet_aux", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ". Now you can use the ", "raw": ". Now you can use the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/TheMistoAI/MistoLine", "href": null, "resource": { "type": "model", "id": "TheMistoAI/MistoLine", "discussionNum": null }, "url": "https://huggingface.co/TheMistoAI/MistoLine", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ControlNet with Diffusers completely.", "raw": " ControlNet with Diffusers completely.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here's a demo for you: ", "raw": "Here's a demo for you: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/radames/MistoLine-ControlNet-demo", "href": null, "resource": { "type": "space", "id": "radames/MistoLine-ControlNet-demo", "discussionNum": null }, "url": "https://huggingface.co/spaces/radames/MistoLine-ControlNet-demo", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Super resolution version: ", "raw": "Super resolution version: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/radames/Enhance-This-HiDiffusion-SDXL", "href": null, "resource": { "type": "space", "id": "radames/Enhance-This-HiDiffusion-SDXL", "discussionNum": null }, "url": "https://huggingface.co/spaces/radames/Enhance-This-HiDiffusion-SDXL", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```python\nfrom controlnet_aux import AnylineDetector\n\nanyline = AnylineDetector.from_pretrained(\n \"TheMistoAI/MistoLine\", filename=\"MTEED.pth\", subfolder=\"Anyline\"\n).to(\"cuda\")\n\nsource = Image.open(\"source.png\")\nresult = anyline(source, detect_resolution=1280)\n```", "href": null, "resource": null, "url": null, "code": "from controlnet_aux import AnylineDetector\n\nanyline = AnylineDetector.from_pretrained(\n \"TheMistoAI/MistoLine\", filename=\"MTEED.pth\", subfolder=\"Anyline\"\n).to(\"cuda\")\n\nsource = Image.open(\"source.png\")\nresult = anyline(source, detect_resolution=1280)", "user": null, "label": null, "lang": "python" } ]
Thanks to @OzzyGT for pushing the new Anyline preprocessor to https://github.com/huggingface/controlnet_aux. Now you can use the https://huggingface.co/TheMistoAI/MistoLine ControlNet with Diffusers completely. Here's a demo for you: https://huggingface.co/spaces/radames/MistoLine-ControlNet-demo Super resolution version: https://huggingface.co/spaces/radames/Enhance-This-HiDiffusion-SDXL ```python from controlnet_aux import AnylineDetector anyline = AnylineDetector.from_pretrained( "TheMistoAI/MistoLine", filename="MTEED.pth", subfolder="Anyline" ).to("cuda") source = Image.open("source.png") result = anyline(source, detect_resolution=1280) ```
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2024-05-23T04:18:35.000Z
2024-05-23T04:19:17.352Z
[]
/posts/radames/604728486309788
5,335
0
570377692548960
[ { "type": "text", "value": "I use mergekit regularly, and often enough get acceptable results without performing fine-tuning afterward. My current thinking is that DARE-TIES should be avoided when merging dense models, as the process of thinning inherently punches holes in models.", "raw": "I use mergekit regularly, and often enough get acceptable results without performing fine-tuning afterward. My current thinking is that DARE-TIES should be avoided when merging dense models, as the process of thinning inherently punches holes in models.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I've had success using SLERP merges to graft Mistral v0.1 models with Mistral v0.2 models to obtain the context length benefits of the latter, and am looking forward to experimenting with Mistral v0.3, which recently dropped.", "raw": "I've had success using SLERP merges to graft Mistral v0.1 models with Mistral v0.2 models to obtain the context length benefits of the latter, and am looking forward to experimenting with Mistral v0.3, which recently dropped.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I use mergekit regularly, and often enough get acceptable results without performing fine-tuning afterward. My current thinking is that DARE-TIES should be avoided when merging dense models, as the process of thinning inherently punches holes in models. I've had success using SLERP merges to graft Mistral v0.1 models with Mistral v0.2 models to obtain the context length benefits of the latter, and am looking forward to experimenting with Mistral v0.3, which recently dropped.
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[]
[]
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2024-05-23T03:19:02.000Z
2024-05-25T21:14:03.330Z
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/posts/grimjim/570377692548960
1,378
1
598312932414376
[ { "type": "text", "value": "Hi HF Community!๐Ÿค—", "raw": "Hi HF Community!๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If you are excited about AlphaFold3, but upset because it is not open-source, I might have a solution to cheer you up a little bit: ", "raw": "If you are excited about AlphaFold3, but upset because it is not open-source, I might have a solution to cheer you up a little bit: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/as-cle-bert/proteinviz", "href": null, "resource": { "type": "space", "id": "as-cle-bert/proteinviz", "discussionNum": null }, "url": "https://huggingface.co/spaces/as-cle-bert/proteinviz", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is a space that lets you predict the 3D structure of proteins from their amino-acidic sequences, with the protein folding model ", "raw": "This is a space that lets you predict the 3D structure of proteins from their amino-acidic sequences, with the protein folding model ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/facebook/esmfold_v1", "href": null, "resource": { "type": "model", "id": "facebook/esmfold_v1", "discussionNum": null }, "url": "https://huggingface.co/facebook/esmfold_v1", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ": using this space is the perfect quick-start to become a Protein Scientist! (or maybe not, who knows...๐Ÿค”)", "raw": ": using this space is the perfect quick-start to become a Protein Scientist! (or maybe not, who knows...๐Ÿค”)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In the meantime, if you are curious about what's going on with AlphaFold3 and want something Biologist๐Ÿ”ฌ/Computer Scientist๐Ÿ’ป-friendly, you can also check out the latest community blog post I wrote: ", "raw": "In the meantime, if you are curious about what's going on with AlphaFold3 and want something Biologist๐Ÿ”ฌ/Computer Scientist๐Ÿ’ป-friendly, you can also check out the latest community blog post I wrote: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/as-cle-bert/what-is-going-on-with-alphafold3", "href": "https://huggingface.co/blog/as-cle-bert/what-is-going-on-with-alphafold3", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ๐Ÿš€", "raw": " ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Have fun and enjoy open-source science!๐Ÿงฌ", "raw": "Have fun and enjoy open-source science!๐Ÿงฌ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hi HF Community!๐Ÿค— If you are excited about AlphaFold3, but upset because it is not open-source, I might have a solution to cheer you up a little bit: https://huggingface.co/spaces/as-cle-bert/proteinviz This is a space that lets you predict the 3D structure of proteins from their amino-acidic sequences, with the protein folding model https://huggingface.co/facebook/esmfold_v1: using this space is the perfect quick-start to become a Protein Scientist! (or maybe not, who knows...๐Ÿค”) In the meantime, if you are curious about what's going on with AlphaFold3 and want something Biologist๐Ÿ”ฌ/Computer Scientist๐Ÿ’ป-friendly, you can also check out the latest community blog post I wrote: https://huggingface.co/blog/as-cle-bert/what-is-going-on-with-alphafold3 ๐Ÿš€ Have fun and enjoy open-source science!๐Ÿงฌ
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[]
[]
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2024-05-23T00:27:10.000Z
2024-05-25T16:15:01.773Z
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/posts/as-cle-bert/598312932414376
1,421
10
714509034080547
[ { "type": "text", "value": "Interesting, I've just seen the my first HF spam on one of my new model uploads: ", "raw": "Interesting, I've just seen the my first HF spam on one of my new model uploads: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/shisa-ai/shisa-v1-llama3-70b", "href": null, "resource": { "type": "model", "id": "shisa-ai/shisa-v1-llama3-70b", "discussionNum": null }, "url": "https://huggingface.co/shisa-ai/shisa-v1-llama3-70b", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - someone has an SEO spam page as a HF space attached to the model!?! Wild. Who do I report this to?", "raw": " - someone has an SEO spam page as a HF space attached to the model!?! Wild. Who do I report this to?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Interesting, I've just seen the my first HF spam on one of my new model uploads: https://huggingface.co/shisa-ai/shisa-v1-llama3-70b - someone has an SEO spam page as a HF space attached to the model!?! Wild. Who do I report this to?
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[]
[]
[]
2024-05-22T17:24:42.000Z
2024-05-24T09:34:18.919Z
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/posts/leonardlin/714509034080547
1,934
4
868344267182453
[ { "type": "text", "value": "I've begun adding valuable blog posts on using/creating synthetic datasets to my curated list. ", "raw": "I've begun adding valuable blog posts on using/creating synthetic datasets to my curated list. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I am starting with a great post by ", "raw": "I am starting with a great post by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@MoritzLaurer", "href": null, "resource": null, "url": null, "code": null, "user": "MoritzLaurer", "label": null, "lang": null }, { "type": "text", "value": " on utilizing an open LLM to generate data for training a specialized Roberta model. ", "raw": " on utilizing an open LLM to generate data for training a specialized Roberta model. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read the blog post: ", "raw": "Read the blog post: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/synthetic-data-save-costs", "href": "https://huggingface.co/blog/synthetic-data-save-costs", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "See the rest of the list: ", "raw": "See the rest of the list: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/davanstrien/awesome-synthetic-datasets", "href": "https://github.com/davanstrien/awesome-synthetic-datasets", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I've begun adding valuable blog posts on using/creating synthetic datasets to my curated list. I am starting with a great post by @MoritzLaurer on utilizing an open LLM to generate data for training a specialized Roberta model. Read the blog post: https://huggingface.co/blog/synthetic-data-save-costs See the rest of the list: https://github.com/davanstrien/awesome-synthetic-datasets
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[]
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2024-05-22T16:05:04.000Z
2024-05-22T16:05:04.592Z
[]
/posts/davanstrien/868344267182453
1,852
0
460363478987667
[ { "type": "text", "value": "We're thrilled to share the latest milestone in our journey toward bringing AISAK to the world: the introduction of AISAK-TVI, our first natively multimodal model.", "raw": "We're thrilled to share the latest milestone in our journey toward bringing AISAK to the world: the introduction of AISAK-TVI, our first natively multimodal model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "As AISAK edges closer to a potential release for users, each advancement, like the development of AISAK-TVI, brings us one step closer to realizing our vision of a comprehensive AI solution. With AISAK-TVI, we're pushing the boundaries of AI capabilities, enabling the processing of both textual and visual inputs with textual output, all within the AISAK ecosystem.", "raw": "As AISAK edges closer to a potential release for users, each advancement, like the development of AISAK-TVI, brings us one step closer to realizing our vision of a comprehensive AI solution. With AISAK-TVI, we're pushing the boundaries of AI capabilities, enabling the processing of both textual and visual inputs with textual output, all within the AISAK ecosystem.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "While the prospect of public, everyday usage of AISAK remains on the horizon, we must acknowledge the reality of operating within constraints of limited resources. The journey to a widespread release demands careful planning, rigorous testing, and ongoing refinement, tasks that require time, dedication, and support.", "raw": "While the prospect of public, everyday usage of AISAK remains on the horizon, we must acknowledge the reality of operating within constraints of limited resources. The journey to a widespread release demands careful planning, rigorous testing, and ongoing refinement, tasks that require time, dedication, and support.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We recognize that achieving our goals requires collaboration and contribution from a diverse community of enthusiasts, experts, and innovators. If you're passionate about AI and eager to be part of our journey, we invite you to lend your expertise, insights, or resources to help accelerate the progress of AISAK.", "raw": "We recognize that achieving our goals requires collaboration and contribution from a diverse community of enthusiasts, experts, and innovators. If you're passionate about AI and eager to be part of our journey, we invite you to lend your expertise, insights, or resources to help accelerate the progress of AISAK.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Whether you're a developer, researcher, investor, or simply someone with a keen interest in shaping the future of AI, your contributions can make a meaningful difference. Reach out to us at [email protected] to explore how you can get involved and contribute to the evolution of AISAK.", "raw": "Whether you're a developer, researcher, investor, or simply someone with a keen interest in shaping the future of AI, your contributions can make a meaningful difference. Reach out to us at [email protected] to explore how you can get involved and contribute to the evolution of AISAK.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Thank you for your continued support and enthusiasm. Together, we're laying the groundwork for a future where AI enriches and empowers lives in ways we've only begun to imagine.", "raw": "Thank you for your continued support and enthusiasm. Together, we're laying the groundwork for a future where AI enriches and empowers lives in ways we've only begun to imagine.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Warm regards,", "raw": "Warm regards,", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Mandela Logan - AISAK Team", "raw": "Mandela Logan - AISAK Team", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/aisak-ai/aisak-tvi", "href": null, "resource": { "type": "model", "id": "aisak-ai/aisak-tvi", "discussionNum": null }, "url": "https://huggingface.co/aisak-ai/aisak-tvi", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/aisak-ai/aisak-65ddeeb08d0978de6114702f", "href": null, "resource": { "type": "collection", "id": "aisak-ai/aisak-65ddeeb08d0978de6114702f", "discussionNum": null }, "url": "https://huggingface.co/collections/aisak-ai/aisak-65ddeeb08d0978de6114702f", "code": null, "user": null, "label": null, "lang": null } ]
We're thrilled to share the latest milestone in our journey toward bringing AISAK to the world: the introduction of AISAK-TVI, our first natively multimodal model. As AISAK edges closer to a potential release for users, each advancement, like the development of AISAK-TVI, brings us one step closer to realizing our vision of a comprehensive AI solution. With AISAK-TVI, we're pushing the boundaries of AI capabilities, enabling the processing of both textual and visual inputs with textual output, all within the AISAK ecosystem. While the prospect of public, everyday usage of AISAK remains on the horizon, we must acknowledge the reality of operating within constraints of limited resources. The journey to a widespread release demands careful planning, rigorous testing, and ongoing refinement, tasks that require time, dedication, and support. We recognize that achieving our goals requires collaboration and contribution from a diverse community of enthusiasts, experts, and innovators. If you're passionate about AI and eager to be part of our journey, we invite you to lend your expertise, insights, or resources to help accelerate the progress of AISAK. Whether you're a developer, researcher, investor, or simply someone with a keen interest in shaping the future of AI, your contributions can make a meaningful difference. Reach out to us at [email protected] to explore how you can get involved and contribute to the evolution of AISAK. Thank you for your continued support and enthusiasm. Together, we're laying the groundwork for a future where AI enriches and empowers lives in ways we've only begun to imagine. Warm regards, Mandela Logan - AISAK Team https://huggingface.co/aisak-ai/aisak-tvi https://huggingface.co/collections/aisak-ai/aisak-65ddeeb08d0978de6114702f
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[]
[]
2024-05-22T15:18:01.000Z
2024-05-22T15:18:01.927Z
[]
/posts/mandelakori/460363478987667
1,524
0
885841437422630
[ { "type": "text", "value": "The kraken has awakened!", "raw": "The kraken has awakened!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A Game-Changer in LLM Flexibility and Performance!", "raw": "A Game-Changer in LLM Flexibility and Performance!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Over the past few weeks, VAGO solutions teamed up with Cognitive Computations and HyperSpace to develop a groundbreaking architecture that redefines flexibility in combining different LLM into one model.", "raw": "Over the past few weeks, VAGO solutions teamed up with Cognitive Computations and HyperSpace to develop a groundbreaking architecture that redefines flexibility in combining different LLM into one model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@fernandofernandes", "href": null, "resource": null, "url": null, "code": null, "user": "fernandofernandes", "label": null, "lang": null }, { "type": "text", "value": " , me, ", "raw": " , me, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Crystalcareai", "href": null, "resource": null, "url": null, "code": null, "user": "Crystalcareai", "label": null, "lang": null }, { "type": "text", "value": " , ", "raw": " , ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@ehartford", "href": null, "resource": null, "url": null, "code": null, "user": "ehartford", "label": null, "lang": null }, { "type": "text", "value": " created the Kraken!", "raw": " created the Kraken!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " What Can It Do? ๐Ÿ™ ", "raw": " What Can It Do? ๐Ÿ™ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… Versatile Architecture: Kraken allows the seamless combination of LLMs with varying sizes, quantizations, and model architectures. It currently supports quantizations in 4-bit, 8-bit, and AWQ, with more on the way. And it runs on Hugging Face Transformers 4.40+", "raw": "โœ… Versatile Architecture: Kraken allows the seamless combination of LLMs with varying sizes, quantizations, and model architectures. It currently supports quantizations in 4-bit, 8-bit, and AWQ, with more on the way. And it runs on Hugging Face Transformers 4.40+", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… Kraken Router: Utilizing a custom sequence classification model with a context length of 32k tokens, The Kraken Router directs inputs to the most suitable Expert based on their characteristics.", "raw": "โœ… Kraken Router: Utilizing a custom sequence classification model with a context length of 32k tokens, The Kraken Router directs inputs to the most suitable Expert based on their characteristics.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… Adaptability: Enhanced input formatting supports the modelโ€™s adaptability to diverse conversational contexts.", "raw": "โœ… Adaptability: Enhanced input formatting supports the modelโ€™s adaptability to diverse conversational contexts.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… Extreme Versatility: Easily swap experts within Kraken for your specific use cases without retraining the entire model. For example, if you've built a Kraken for coding in Python you can upgrade your Python model without retraining the router or add a C# model by retraining the router.", "raw": "โœ… Extreme Versatility: Easily swap experts within Kraken for your specific use cases without retraining the entire model. For example, if you've built a Kraken for coding in Python you can upgrade your Python model without retraining the router or add a C# model by retraining the router.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… Open Source Pipeline: Weโ€™re sharing the entire pipeline, including router creation, training, architecture setup, and Kraken inference, on JupyterNotebooks: ", "raw": "โœ… Open Source Pipeline: Weโ€™re sharing the entire pipeline, including router creation, training, architecture setup, and Kraken inference, on JupyterNotebooks: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/cognitivecomputations/kraken", "href": "https://github.com/cognitivecomputations/kraken", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Kraken marks the beginning of an exciting new journey in #OpenSource LLM. Why? Because it empowers the open source community in accelerating the catch-up process to proprietary LLMs like #GPT and #Claude ๐Ÿคฉ", "raw": "Kraken marks the beginning of an exciting new journey in #OpenSource LLM. Why? Because it empowers the open source community in accelerating the catch-up process to proprietary LLMs like #GPT and #Claude ๐Ÿคฉ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We proudly introduce the very first 2 Kraken models, that integrates top-tier LLM and Multilingual capabilities: ", "raw": "We proudly introduce the very first 2 Kraken models, that integrates top-tier LLM and Multilingual capabilities: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/cognitivecomputations/Kraken", "href": null, "resource": { "type": "model", "id": "cognitivecomputations/Kraken", "discussionNum": null }, "url": "https://huggingface.co/cognitivecomputations/Kraken", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/VAGOsolutions/Kraken-Multilingual", "href": null, "resource": { "type": "model", "id": "VAGOsolutions/Kraken-Multilingual", "discussionNum": null }, "url": "https://huggingface.co/VAGOsolutions/Kraken-Multilingual", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " Right now it's supported by Hugging Face transformers library. Would love to see the integration into VLM and TGWI!", "raw": " Right now it's supported by Hugging Face transformers library. Would love to see the integration into VLM and TGWI!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
The kraken has awakened! A Game-Changer in LLM Flexibility and Performance! Over the past few weeks, VAGO solutions teamed up with Cognitive Computations and HyperSpace to develop a groundbreaking architecture that redefines flexibility in combining different LLM into one model. @fernandofernandes , me, @Crystalcareai , @ehartford created the Kraken! What Can It Do? ๐Ÿ™ โœ… Versatile Architecture: Kraken allows the seamless combination of LLMs with varying sizes, quantizations, and model architectures. It currently supports quantizations in 4-bit, 8-bit, and AWQ, with more on the way. And it runs on Hugging Face Transformers 4.40+ โœ… Kraken Router: Utilizing a custom sequence classification model with a context length of 32k tokens, The Kraken Router directs inputs to the most suitable Expert based on their characteristics. โœ… Adaptability: Enhanced input formatting supports the modelโ€™s adaptability to diverse conversational contexts. โœ… Extreme Versatility: Easily swap experts within Kraken for your specific use cases without retraining the entire model. For example, if you've built a Kraken for coding in Python you can upgrade your Python model without retraining the router or add a C# model by retraining the router. โœ… Open Source Pipeline: Weโ€™re sharing the entire pipeline, including router creation, training, architecture setup, and Kraken inference, on JupyterNotebooks: https://github.com/cognitivecomputations/kraken Kraken marks the beginning of an exciting new journey in #OpenSource LLM. Why? Because it empowers the open source community in accelerating the catch-up process to proprietary LLMs like #GPT and #Claude ๐Ÿคฉ We proudly introduce the very first 2 Kraken models, that integrates top-tier LLM and Multilingual capabilities: https://huggingface.co/cognitivecomputations/Kraken https://huggingface.co/VAGOsolutions/Kraken-Multilingual Right now it's supported by Hugging Face transformers library. Would love to see the integration into VLM and TGWI!
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2024-05-22T15:07:07.000Z
2024-05-22T15:15:07.824Z
[]
/posts/DavidGF/885841437422630
1,563
0
115164605711086
[ { "type": "text", "value": "we recently shipped fine-grained access tokens on Hugging Face Hub, which lets you create tokens with super specific permissions", "raw": "we recently shipped fine-grained access tokens on Hugging Face Hub, which lets you create tokens with super specific permissions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "for instance, if you want to collaborate with an external organization you don't want to use your write token since they can access everything you can access. instead you can set token access to repositories under that org only like below ", "raw": "for instance, if you want to collaborate with an external organization you don't want to use your write token since they can access everything you can access. instead you can set token access to repositories under that org only like below ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
we recently shipped fine-grained access tokens on Hugging Face Hub, which lets you create tokens with super specific permissions for instance, if you want to collaborate with an external organization you don't want to use your write token since they can access everything you can access. instead you can set token access to repositories under that org only like below
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2024-05-22T14:30:16.000Z
2024-05-22T14:30:16.855Z
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/posts/merve/115164605711086
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