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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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Falcon has landed... again! ",
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Falcon has landed... again! ",
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] | ๐ฆ
Falcon has landed... again!
And now it not just reads but sees as well ๐๐
Here is a summary of the Falcon-11B-VLM model:
Model Type: Causal decoder-only model ๐.
Parameters: 11 billion ๐.
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 ๐ผ๏ธ๐.
Training: Pretrained on over 5,000 billion tokens from RefinedWeb with curated corpora ๐.
Dynamic Encoding: Enhances perception of fine-grained details in images ๐.
Training Hardware: 16 A100 80GB GPUs with ZeRO and Flash-Attention 2 ๐ฅ๏ธ.
Tokenizer: Falcon-7B/11B tokenizer ๐งฉ.
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License: Open Source - TII Falcon License 2.0, based on Apache 2.0 ๐.
Model: https://huggingface.co/tiiuae/falcon-11B-vlm | {
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] | ChatGPT made Custom GPTs Free for Everyone.
Yes, you can use them but...
with limitations like
You can't use DallE ๐ฅ,
You can't make Custom GPTs
And chat limit also๐ฅ.
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 ๐: | {
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] | 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
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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:
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] | Started a new AI Session: The AI Paper Talk Show ๐ง ๐ค๐ฅ
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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Here's what the pipeline offers:
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- **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.
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A new paper (by @HuanjinYao et al) built a dense connector that does it better! https://huggingface.co/spaces/HuanjinYao/DenseConnector-v1.5-8B
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] | 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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] | You are happy that @mistralai is releasing a new model ๐
You become even more happy to see it's a completely new coding model ๐
Then you become sad because the model is licensed under MNLP ๐
Before we talk about MNLP, here is the gist of the model:
๐ท๏ธName: Codestral (Code + Mistral ๐)
๐ 22B parameters
๐ Supports 80 programming languages (including Python, Java, C, C++, bash, swift, and more)
๐ Outperforms Llama 3 70B and Code Llama 70B on HumanEval and MBPP
๐ Outperforms DeepSeek Coder 33B on HumanEval
๐ 32K context window (longer than Llama 3, DeepSeek, or Code Llama)
๐ค 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... ๐ There is another model that's open source and actually gives better performance on HumanEval: https://huggingface.co/Bin12345/AutoCoder | {
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] | What We Learned from a Year of Building with LLMs
It's a nice perspective outlined in here.
โWhen a measure becomes a target, it ceases to be a good measure.โ
โ Goodhartโs Law
https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/ | {
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] | ๐ 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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Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐บ๐ธ",
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Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐บ๐ธ",
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"value": "๐ Keywords: #3DAvatars #RealTimeRendering #RelightableAvatars #3DModeling #VirtualReality #CVPR2024 #DeepLearning #ComputerGraphics #ComputerVision #Innovation #VR",
"raw": "๐ Keywords: #3DAvatars #RealTimeRendering #RelightableAvatars #3DModeling #VirtualReality #CVPR2024 #DeepLearning #ComputerGraphics #ComputerVision #Innovation #VR",
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] | ๐๐ญ๐ New Research Alert - CVPR 2024 (Avatars Collection)! ๐๐ญ๐
๐ Title: Relightable Gaussian Codec Avatars ๐
๐ 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.
๐ฅ Authors: @psyth, @GBielXONE02, Tomas Simon, Junxuan Li, and @giljoonam
๐
Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐บ๐ธ
๐ Paper: https://huggingface.co/papers/2312.03704
๐ GitHub Page: https://shunsukesaito.github.io/rgca/
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
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] | # 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?

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

## 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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"value": "Successfully defended my thesis yesterday ๐",
"raw": "Successfully defended my thesis yesterday ๐",
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"value": "Glad that my supervisor gets the innovation behind it - โAn Adaptive Virtual Intelligent Tutor that autonomously learns and adjusts to your learning preferencesโ",
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"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 studentโs 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 studentโs learning style) ",
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"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.",
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"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.",
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] | Successfully defended my thesis yesterday ๐
Glad that my supervisor gets the innovation behind it - โAn 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 studentโs 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 todayโs AI systems - Autonomously Adaptive Assistants (AAA) - and oh Iโm currently writing a paper on this:) | {
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"value": " โWhat 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": " โWhat 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!",
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LaVague: found the latest paper (ConvLlaVA which is dope by the way ",
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LaVague: found the latest paper (ConvLlaVA which is dope by the way ",
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"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 (",
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"raw": ") so donโt miss it ๐",
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] | ๐ชBuild 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:
โWhat 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!
โGemini: found a paper from Jan 29, 2024
โOpenAI: found a paper from October 2023
โYou.com: found a paper from Jan 29 2024
โ
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 donโt miss it ๐
You can also start playing with our framework: https://github.com/lavague-ai/LaVague | {
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] | 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. ๐ค
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โถ๏ธ To get started go to: https://huggingface.co/spaces/CohereForAI/MMLU-evaluation
๐ They also have an Aya Discord server for collaboration with other participants: https://discord.gg/9gVhdfnQMN
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But I'm #data_rich ๐ | {
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] | ๐ฃ๐ฟ๐ผ๐๐ผ๐๐๐ฝ๐ถ๐ป๐ด 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 ๐๐ฟ๐ฎ๐ฑ๐ถ๐ผ and will unveil something new on June 6th: https://www.youtube.com/watch?v=44vi31hehw4&ab_channel=HuggingFace
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"value": "๐ค everything-ai is natively a multi-tasking agent, 100% local, that is able to perform several AI-related tasks",
"raw": "๐ค everything-ai is natively a multi-tasking agent, 100% local, that is able to perform several AI-related tasks",
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"value": "๐ I am more than thrilled to introduce some new functionalities that were added since last release:",
"raw": "๐ I am more than thrilled to introduce some new functionalities that were added since last release:",
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"value": "- ๐๏ธ๐ 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": "- ๐๏ธ๐ Handle audio files or microphone recordings, classifying or transcribing them with almost every audio-classification and automatic-speech-recognition model on Hugging Face Hub.",
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"value": "- ๐ฝ๏ธ Generate video from text prompts with almost every text-to-video model on HuggingFace Hub (original architecture by [Vasiliy Katsyka](",
"raw": "- ๐ฝ๏ธ Generate video from text prompts with almost every text-to-video model on HuggingFace Hub (original architecture by [Vasiliy Katsyka](",
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"value": "- ๐งฌ Predict the 3D structure of proteins from their amino-acidic sequence, with EsmFold by AI at Meta ([demo](",
"raw": "- ๐งฌ Predict the 3D structure of proteins from their amino-acidic sequence, with EsmFold by AI at Meta ([demo](",
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"value": "- ๐๏ธ Finetune HF models on several downstream tasks with AutoTrain local integration (AutoTrain is developed by [Abhishek Thakur](",
"raw": "- ๐๏ธ Finetune HF models on several downstream tasks with AutoTrain local integration (AutoTrain is developed by [Abhishek Thakur](",
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"raw": "- ๐ฃ๏ธ Unleash powerful LLMs and exploit larger database collections for RAG with the integration of Hugging Face Spaces API and Supabase PostgreSQL databases ([demo](",
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] | everything-ai v2.0.1: more AI power on your Desktop
What is everything-ai?
๐ค everything-ai is natively a multi-tasking agent, 100% local, that is able to perform several AI-related tasks
What's new?
๐ I am more than thrilled to introduce some new functionalities that were added since last release:
- ๐๏ธ๐ Handle audio files or microphone recordings, classifying or transcribing them with almost every audio-classification and automatic-speech-recognition model on Hugging Face Hub.
- ๐ฝ๏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))
- ๐งฌ 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))
- ๐๏ธ Finetune HF models on several downstream tasks with AutoTrain local integration (AutoTrain is developed by [Abhishek Thakur](https://github.com/abhishekkrthakur))
- ๐ฃ๏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`!๐
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!
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Falcon VLM",
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Falcon VLM",
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] | Weekly highlights for the HF ecosystem!
๐ Phi 3
๐ฆ
Falcon VLM
๐ค sentence-transformers v3.0 is here! Train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks and more!
๐ฅณ 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!
โจ Tools now available in HuggingChat! Use any AI apps built by the community! ๐ฅ
๐ง 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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] | ๐งจ Diffusers 0.28.0 is out ๐ฅ
It features the first non-generative pipeline of the library -- Marigold ๐ฅ
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 ๐ค
Check out the release notes here:
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"value": "I experimented with a new approach that is both useful and fun. It can help you overcome writerโs 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!",
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"value": "1๏ธโฃ First, I scraped all the blog posts written on Hugging Face to create a dataset with the headlines, texts, dates, and authors' names.",
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"value": "4๏ธโฃ 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.",
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"value": "๐๐ก๐ฒ ๐ข๐ญ ๐ฆ๐๐ญ๐ญ๐๐ซ๐ฌ. 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": "๐๐ก๐ฒ ๐ข๐ญ ๐ฆ๐๐ญ๐ญ๐๐ซ๐ฌ. 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.",
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"value": "These tweaks can have a big impact on your position in search engines and, therefore, on your traffic. Itโs 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. Itโs also good leverage for our creativity since you can compare the initial idea with another one from an outside perspective.",
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"value": "Imagine if youโre a large news organization; you could run this experiment with thousands of news articles.",
"raw": "Imagine if youโre a large news organization; you could run this experiment with thousands of news articles.",
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I experimented with a new approach that is both useful and fun. It can help you overcome writerโs 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๏ธโฃ First, I scraped all the blog posts written on Hugging Face to create a dataset with the headlines, texts, dates, and authors' names.
2๏ธโฃ 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๏ธโฃ Then, I used a dataset generation workflow built by @davanstrien to generate a DPO dataset.
4๏ธโฃ 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.
๐๐ก๐ฒ ๐ข๐ญ ๐ฆ๐๐ญ๐ญ๐๐ซ๐ฌ. 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. Itโs also good leverage for our creativity since you can compare the initial idea with another one from an outside perspective.
Imagine if youโre 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.
๐ Take a look at it and rate the headlines https://huggingface.co/spaces/fdaudens/journalism-argilla-space
๐ Daniel's code https://github.com/huggingface/data-is-better-together/blob/main/dpo/README.md | {
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"raw": " released Falcon 11B Vision Model !",
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๐ฆ
๐๐",
"raw": "๐ฆ
๐ฆ
๐๐",
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] | ๐๐ปโโ๏ธ Hey there folks ,
@tiiuae released Falcon 11B Vision Model !
๐ฆ
๐ฆ
๐๐
it's quite good , and you can try it here : https://huggingface.co/spaces/Tonic/Falcon-Vision | {
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"value": "Despite getting much less $$, recognition & visibility than entrepreneurs, the scientists who publish their groundbreaking research openly are the cornerstone of technological progress & massively contribute to making the world a better place!",
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Despite getting much less $$, 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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] | โผ๏ธSentence 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๏ธโฃ 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๏ธโฃ 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!
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4๏ธโฃ Hyperparameter Optimization
Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task.
5๏ธโฃ 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
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] | 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.๐
Join us on the Hugging Face Discord channel for the Hangout!
https://discord.gg/hugging-face-879548962464493619?event=1243129304863215656 ๐ค | {
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] | 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:
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] | Remember stacking in ensemble ML? ๐ค
What happens if you do the reverse of that but with LLMs? ๐คฏ
Basically, MoE created by merging multiple models (instead of being pre-trained like Mixtral)? ๐ง
Frankenstein MoE! (not an official name) ๐งโโ๏ธ
That's the new Kraken architecture! ๐
It uses a sequence classification model to route inputs to the most suitable language model based on the input's characteristics. ๐ฆ
Yup, multiple full-fledged LLMs are loaded into memory, and then a classification layer decides who gets to generate an output! ๐ฐ
Tell me you have too many GPUs without telling me you have too many GPUs! ๐ฅ๏ธ๐ฅ
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? ๐คทโโ๏ธ
Model: https://huggingface.co/cognitivecomputations/Kraken
Github: https://github.com/cognitivecomputations/kraken
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"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```",
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"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)",
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"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.",
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] | 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 โก
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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] | ๐๐ญ๐ New Research Alert - InstructAvatar (Avatars Collection)! ๐๐ญ๐
๐ Title: InstructAvatar: Text-Guided Emotion and Motion Control for Avatar Generation ๐
๐ 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.
๐ฅ Authors: Yuchi Wang et al.
๐ Paper: https://huggingface.co/papers/2405.15758
๐ Github Page: https://wangyuchi369.github.io/InstructAvatar/
๐ Repository: https://github.com/wangyuchi369/InstructAvatar
๐ More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
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๐๐๐ ๐ช",
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๐๐๐ ๐ช",
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"value": " that paper ๐๐น๐ฆ๐ค๐ถ๐ต๐ข๐ฃ๐ญ๐ฆ ๐๐ฐ๐ฅ๐ฆ ๐๐ค๐ต๐ช๐ฐ๐ฏ๐ด ๐๐ญ๐ช๐ค๐ช๐ต ๐๐ฆ๐ต๐ต๐ฆ๐ณ ๐๐๐ ๐๐จ๐ฆ๐ฏ๐ต๐ด was accepted at ICLR 2024! ",
"raw": " that paper ๐๐น๐ฆ๐ค๐ถ๐ต๐ข๐ฃ๐ญ๐ฆ ๐๐ฐ๐ฅ๐ฆ ๐๐ค๐ต๐ช๐ฐ๐ฏ๐ด ๐๐ญ๐ช๐ค๐ช๐ต ๐๐ฆ๐ต๐ต๐ฆ๐ณ ๐๐๐ ๐๐จ๐ฆ๐ฏ๐ต๐ด was accepted at ICLR 2024! ",
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"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.",
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"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.",
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"value": "๐งโ๐ป 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": "๐งโ๐ป 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.",
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"value": "So the agent system will prompt an agent with \"Now you can use these tools: calculator, search,...\"",
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"value": "But ๐๐ค๐ฌ ๐จ๐๐ค๐ช๐ก๐ ๐ฉ๐๐ ๐๐๐๐ฃ๐ฉ ๐๐ญ๐ฅ๐ง๐๐จ๐จ ๐๐ฉ๐จ ๐๐๐ฉ๐๐ค๐ฃ๐จ?",
"raw": "But ๐๐ค๐ฌ ๐จ๐๐ค๐ช๐ก๐ ๐ฉ๐๐ ๐๐๐๐ฃ๐ฉ ๐๐ญ๐ฅ๐ง๐๐จ๐จ ๐๐ฉ๐จ ๐๐๐ฉ๐๐ค๐ฃ๐จ?",
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"raw": "We ๐ฝ๐ฟ๐ฒ๐ณ๐ฒ๐ฟ๐ฟ๐ฒ๐ฑ ๐๐ผ ๐ด๐ผ ๐๐ถ๐๐ต ๐ณ๐ผ๐ฟ๐บ๐๐น๐ฎ๐๐ถ๐ป๐ด ๐ฎ๐ฐ๐๐ถ๐ผ๐ป๐ ๐ถ๐ป ๐๐ผ๐ฑ๐ฒ, ๐๐ต๐ถ๐ฐ๐ต ๐ถ๐ ๐บ๐๐ฐ๐ต ๐บ๐ผ๐ฟ๐ฒ ๐๐ฒ๐ฟ๐๐ฎ๐๐ถ๐น๐ฒ ๐ฎ๐ป๐ฑ ๐ฐ๐ผ๐ป๐ฐ๐ถ๐๐ฒ, ๐ฎ๐ป๐ฑ ๐ฎ๐น๐น๐ผ๐๐ ๐๐ผ ๐ฐ๐ต๐ฎ๐ถ๐ป ๐ฎ๐ฐ๐๐ถ๐ผ๐ป๐ ๐๐ฒ๐ฎ๐บ๐น๐ฒ๐๐๐น๐: see the picture attached for an example where Code formulation really shines.",
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"value": "And the paper confirms our choice: researchers show that ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ฟ๐ฒ๐ฑ ๐๐ผ ๐๐ฆ๐ข๐ก ๐ผ๐ฟ ๐ฝ๐น๐ฎ๐ถ๐ป ๐๐ฒ๐
๐, ๐๐ผ๐ฑ๐ฒ ๐ถ๐ ๐ฏ๐ฒ๐๐๐ฒ๐ฟ ๐ฏ๐ผ๐๐ต ๐ถ๐ป ๐ฐ๐ผ๐ป๐ฐ๐ถ๐๐ฒ๐ป๐ฒ๐๐ ๐ฎ๐ป๐ฑ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ:",
"raw": "And the paper confirms our choice: researchers show that ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ฟ๐ฒ๐ฑ ๐๐ผ ๐๐ฆ๐ข๐ก ๐ผ๐ฟ ๐ฝ๐น๐ฎ๐ถ๐ป ๐๐ฒ๐
๐, ๐๐ผ๐ฑ๐ฒ ๐ถ๐ ๐ฏ๐ฒ๐๐๐ฒ๐ฟ ๐ฏ๐ผ๐๐ต ๐ถ๐ป ๐ฐ๐ผ๐ป๐ฐ๐ถ๐๐ฒ๐ป๐ฒ๐๐ ๐ฎ๐ป๐ฑ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ:",
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"value": "โค Up to 30% fewer steps for the same actions (much more concise)",
"raw": "โค Up to 30% fewer steps for the same actions (much more concise)",
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] | ๐๐ง๐๐ฉ๐๐ฃ๐ ๐ฉ๐ค๐ค๐ก ๐๐๐ก๐ก๐จ ๐๐ฃ ๐๐ค๐๐ ๐๐ช๐จ๐ฉ ๐ฌ๐ค๐ง๐ ๐จ ๐๐๐ฉ๐ฉ๐๐ง ๐ฉ๐๐๐ฃ ๐
๐๐๐ ๐ช
I was really happy to learn today by @sergeipetrov that paper ๐๐น๐ฆ๐ค๐ถ๐ต๐ข๐ฃ๐ญ๐ฆ ๐๐ฐ๐ฅ๐ฆ ๐๐ค๐ต๐ช๐ฐ๐ฏ๐ด ๐๐ญ๐ช๐ค๐ช๐ต ๐๐ฆ๐ต๐ต๐ฆ๐ณ ๐๐๐ ๐๐จ๐ฆ๐ฏ๐ต๐ด 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.
๐งโ๐ป 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.
๐It 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 ๐๐ค๐ฌ ๐จ๐๐ค๐ช๐ก๐ ๐ฉ๐๐ ๐๐๐๐ฃ๐ฉ ๐๐ญ๐ฅ๐ง๐๐จ๐จ ๐๐ฉ๐จ ๐๐๐ฉ๐๐ค๐ฃ๐จ?
All well known frameworks let agents write their actions as JSON strings.
We ๐ฝ๐ฟ๐ฒ๐ณ๐ฒ๐ฟ๐ฟ๐ฒ๐ฑ ๐๐ผ ๐ด๐ผ ๐๐ถ๐๐ต ๐ณ๐ผ๐ฟ๐บ๐๐น๐ฎ๐๐ถ๐ป๐ด ๐ฎ๐ฐ๐๐ถ๐ผ๐ป๐ ๐ถ๐ป ๐๐ผ๐ฑ๐ฒ, ๐๐ต๐ถ๐ฐ๐ต ๐ถ๐ ๐บ๐๐ฐ๐ต ๐บ๐ผ๐ฟ๐ฒ ๐๐ฒ๐ฟ๐๐ฎ๐๐ถ๐น๐ฒ ๐ฎ๐ป๐ฑ ๐ฐ๐ผ๐ป๐ฐ๐ถ๐๐ฒ, ๐ฎ๐ป๐ฑ ๐ฎ๐น๐น๐ผ๐๐ ๐๐ผ ๐ฐ๐ต๐ฎ๐ถ๐ป ๐ฎ๐ฐ๐๐ถ๐ผ๐ป๐ ๐๐ฒ๐ฎ๐บ๐น๐ฒ๐๐๐น๐: see the picture attached for an example where Code formulation really shines.
And the paper confirms our choice: researchers show that ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ฟ๐ฒ๐ฑ ๐๐ผ ๐๐ฆ๐ข๐ก ๐ผ๐ฟ ๐ฝ๐น๐ฎ๐ถ๐ป ๐๐ฒ๐
๐, ๐๐ผ๐ฑ๐ฒ ๐ถ๐ ๐ฏ๐ฒ๐๐๐ฒ๐ฟ ๐ฏ๐ผ๐๐ต ๐ถ๐ป ๐ฐ๐ผ๐ป๐ฐ๐ถ๐๐ฒ๐ป๐ฒ๐๐ ๐ฎ๐ป๐ฑ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ:
โค Up to 30% fewer steps for the same actions (much more concise)
โค Up to 20% higher performance on benchmarks
And we find additional benefits, for instance a natural handling of variables.
Read the paper here ๐ https://huggingface.co/papers/2402.01030
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] | We will be providing ZeroGPU grants (for Spaces inference) to those who want to fine-tune PaliGemma and build a Space ๐ฅ
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
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365623455051476 | [
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"value": "Quel impact de lโIA sur les filiรจres du cinรฉma, de lโaudiovisuel et du jeu vidรฉo? ",
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"raw": "Etude prospective ร destination des professionnels ",
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"value": "Si lโIntelligence Artificielle (IA) est utilisรฉe de longue date dans les secteurs du cinรฉma, de lโaudiovisuel et du jeu vidรฉo, les nouvelles applications de lโIA 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 lโIntelligence Artificielle (IA) est utilisรฉe de longue date dans les secteurs du cinรฉma, de lโaudiovisuel et du jeu vidรฉo, les nouvelles applications de lโIA 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.",
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"value": "Le CNC a donc dรฉcider de lancer un nouvel Observatoire de lโIA Afin de mieux comprendre les usages de lโIA et ses impacts rรฉels sur la filiรจre de lโimage. 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 lโIA ร chaque รฉtape du processus de crรฉation et de diffusion dโune ลuvre, en identifiant les opportunitรฉs et risques associรฉs, notamment en termes de mรฉtiers et dโemploi. Cette รฉtude CNC / Bearing Point en a prรฉsentรฉ les principaux enseignements le 6 mars, lors de la journรฉe CNC ยซ Crรฉer, produire, diffuser ร lโheure de lโintelligence artificielle ยป.",
"raw": "Le CNC a donc dรฉcider de lancer un nouvel Observatoire de lโIA Afin de mieux comprendre les usages de lโIA et ses impacts rรฉels sur la filiรจre de lโimage. 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 lโIA ร chaque รฉtape du processus de crรฉation et de diffusion dโune ลuvre, en identifiant les opportunitรฉs et risques associรฉs, notamment en termes de mรฉtiers et dโemploi. Cette รฉtude CNC / Bearing Point en a prรฉsentรฉ les principaux enseignements le 6 mars, lors de la journรฉe CNC ยซ Crรฉer, produire, diffuser ร lโheure de lโintelligence artificielle ยป.",
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"raw": "Le CNC publie la version augmentรฉe de la cartographie des usages de lโIA dans les filiรจres du cinรฉma, de lโaudiovisuel et du jeu vidรฉo.",
"href": null,
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"value": "Lien vers la cartographie complรจte: ",
"raw": "Lien vers la cartographie complรจte: ",
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"href": "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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] | ๐ซ๐ท
Quel impact de lโIA sur les filiรจres du cinรฉma, de lโaudiovisuel et du jeu vidรฉo?
Etude prospective ร destination des professionnels
โ CNC & BearingPoint | 09/04/2024
Si lโIntelligence Artificielle (IA) est utilisรฉe de longue date dans les secteurs du cinรฉma, de lโaudiovisuel et du jeu vidรฉo, les nouvelles applications de lโIA 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 lโIA Afin de mieux comprendre les usages de lโIA et ses impacts rรฉels sur la filiรจre de lโimage. 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 lโIA ร chaque รฉtape du processus de crรฉation et de diffusion dโune ลuvre, en identifiant les opportunitรฉs et risques associรฉs, notamment en termes de mรฉtiers et dโemploi. Cette รฉtude CNC / Bearing Point en a prรฉsentรฉ les principaux enseignements le 6 mars, lors de la journรฉe CNC ยซ Crรฉer, produire, diffuser ร lโheure de lโintelligence artificielle ยป.
Le CNC publie la version augmentรฉe de la cartographie des usages de lโIA dans les filiรจres du cinรฉma, de lโaudiovisuel 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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```
const api_url = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B";
const payload = JSON.stringify({
"query": input,
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const body = {
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return xmlHttp.responseText;
```
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] | 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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] | 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 ๐, and now we have the next version...
@MistralAI Mistral -7B-v0.3 (base) ๐ and Mistral-7B-Instruct-v0.3 ๐ ๏ธ
- 7.3 billion parameters ๐ง
- Apache 2.0 license ๐
- Extended vocabulary of 32,768 ๐
- Supports new v3 Tokenizer and function calling ๐ค
- Also, it's completely uncensored ๐
In conclusion, Mistral-7B-v0.3 is an uncensored Mistral-7B-v0.2 with an extended vocabulary ๐.
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! ๐
Models:
https://huggingface.co/mistralai/Mistral-7B-v0.3
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] | Integrating the French Taxation Embedding Benchmark Task (beta) into the MTEB ๐ค
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 ๐ค 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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"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.",
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"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.",
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"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.",
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"value": "With these terms in mind, letโs explore the idea of a unitary modelโa 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.",
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] | # 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, letโs explore the idea of a unitary modelโa 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. Itโs a vision of unity in diversity, where every piece of knowledge is interconnected through a single, elegant framework.
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] | 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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] | Hi HF Community!๐ค
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!)๐ฅฐ
May you have fun and luck with your protein research!๐งฌ | {
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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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] | 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
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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. ๐ค
Thanks! | {
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] | Why Apache 2.0 Matters for LLMs ๐ค
@01AI_Yi recently switched from a permissive & commercially friendly license, to Apache 2.0. And the community loved it! ๐
@JustinLin610 also had a poll on model license and the majority votes for Apache 2.0.
Why it is a Big Deal? โฌ๏ธ
๐ Legal Simplicity: Custom licenses need costly & time-consuming legal review. Apache 2.0 is well-known & easier for legal teams to handle.
๐ฉโ๐ป 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.
๐ Easier Integration: Apache 2.0 is compatible with many other licenses, simplifying tasks like model merging with models of different licensing requirements.
๐ซ 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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It has also completed disrupted the Chines LLM market and forcing the competitors to drop the price to 1% of the original price.
---
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DeepSeek V2 introduces optimizations to both:
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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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**Searchable Models:** Creative, Balanced, Precise
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https://huggingface.co/spaces/NiansuhAI/LLMs1
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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
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] | ๐ฅ๐๐ New Research Alert - YOLOv10! ๐๐๐ฅ
๐ Title: YOLOv10: Real-Time End-to-End Object Detection ๐
๐ 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.
๐ฅ Authors: Ao Wang et al.
๐ Paper: https://huggingface.co/papers/2405.14458
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๐ฅ Model ๐ค: https://huggingface.co/kadirnar/Yolov10
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๐ฎ Post about YOLOv9 - https://huggingface.co/posts/DmitryRyumin/519784698531054
๐ More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
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"value": "Looks like the good folks at Cohere did not sleep after the success of command r & command r plus! ๐ดโก๏ธ๐",
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] | When was the last time you looked for a non-English LLM, only to be saddened there is NO good option? ๐
For instance, @Meta 's poster child Llama 3 has less than 5% non-English tokens! ๐
@cohere is here to change that... and change they will. ๐ช
Introducing Aya (this time the name actually makes sense) ๐
Aya is an open-weight (CC-BY-NC) model, that comes in 3 flavours:
1๏ธโฃ Aya 101: 13B parameters, mT5-xxl based model that supports 101 languages ๐
2๏ธโฃ Aya 23: 8B and 3๏ธโฃ35B parameter models, supports 23 languages ๐
23 languages covered are: Arabic ๐ธ๐ฆ, Chinese (simplified & traditional) ๐จ๐ณ, Czech ๐จ๐ฟ, Dutch ๐ณ๐ฑ, English ๐ฌ๐ง, French ๐ซ๐ท, German ๐ฉ๐ช, Greek ๐ฌ๐ท, Hebrew ๐ฎ๐ฑ, Hindi ๐ฎ๐ณ, Indonesian ๐ฎ๐ฉ, Italian ๐ฎ๐น, Japanese ๐ฏ๐ต, Korean ๐ฐ๐ท, Persian ๐ฎ๐ท, Polish ๐ต๐ฑ, Portuguese ๐ต๐น, Romanian ๐ท๐ด, Russian ๐ท๐บ, Spanish ๐ช๐ธ, Turkish ๐น๐ท, Ukrainian ๐บ๐ฆ, and Vietnamese ๐ป๐ณ.
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. ๐
These annotations were provided by people across the globe. Not gonna lie, I almost shed a tear reading this... ๐ข
From Cohere: The word Aya is derived from the Twi language meaning โfernโ - a symbol of endurance and resourcefulness. Aya embodies our dedication to advancing multilingual AI. ๐ฟ
Looks like the good folks at Cohere did not sleep after the success of command r & command r plus! ๐ดโก๏ธ๐
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
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"value": "โก๏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": "โก๏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.",
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"value": "๐๏ธ ๐๐ผ๐ป๐๐๐ฟ๐ฎ๐ถ๐ป๐ฒ๐ฑ ๐ฑ๐ฒ๐ฐ๐ผ๐ฑ๐ถ๐ป๐ด is a great technique to generate structured output: you can specify a grammar (=set of rules) that the output should follow, and ๐ฐ๐ผ๐ป๐๐๐ฟ๐ฎ๐ถ๐ป๐ฒ๐ฑ ๐ฑ๐ฒ๐ฐ๐ผ๐ฑ๐ถ๐ป๐ด ๐๐ต๐ฒ๐ป ๐ณ๐ผ๐ฟ๐ฐ๐ฒ๐ ๐๐ต๐ฒ ๐ฑ๐ฒ๐ฐ๐ผ๐ฑ๐ฒ๐ฟ ๐๐ผ ๐ผ๐ป๐น๐ ๐ฝ๐ถ๐ฐ๐ธ ๐๐ผ๐ธ๐ฒ๐ป๐ ๐๐ต๐ฎ๐ ๐ฟ๐ฒ๐๐ฝ๐ฒ๐ฐ๐ ๐๐ผ๐๐ฟ ๐ด๐ฟ๐ฎ๐บ๐บ๐ฎ๐ฟ.",
"raw": "๐๏ธ ๐๐ผ๐ป๐๐๐ฟ๐ฎ๐ถ๐ป๐ฒ๐ฑ ๐ฑ๐ฒ๐ฐ๐ผ๐ฑ๐ถ๐ป๐ด is a great technique to generate structured output: you can specify a grammar (=set of rules) that the output should follow, and ๐ฐ๐ผ๐ป๐๐๐ฟ๐ฎ๐ถ๐ป๐ฒ๐ฑ ๐ฑ๐ฒ๐ฐ๐ผ๐ฑ๐ถ๐ป๐ด ๐๐ต๐ฒ๐ป ๐ณ๐ผ๐ฟ๐ฐ๐ฒ๐ ๐๐ต๐ฒ ๐ฑ๐ฒ๐ฐ๐ผ๐ฑ๐ฒ๐ฟ ๐๐ผ ๐ผ๐ป๐น๐ ๐ฝ๐ถ๐ฐ๐ธ ๐๐ผ๐ธ๐ฒ๐ป๐ ๐๐ต๐ฎ๐ ๐ฟ๐ฒ๐๐ฝ๐ฒ๐ฐ๐ ๐๐ผ๐๐ฟ ๐ด๐ฟ๐ฎ๐บ๐บ๐ฎ๐ฟ.",
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"value": "I've created a guide to show you how to use it, both via our Inference API and locally using ๐ฐ๐ถ๐ต๐ญ๐ช๐ฏ๐ฆ๐ด!",
"raw": "I've created a guide to show you how to use it, both via our Inference API and locally using ๐ฐ๐ถ๐ต๐ญ๐ช๐ฏ๐ฆ๐ด!",
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] | ๐๐๐ฐ ๐ ๐ฎ๐ข๐๐ ๐ข๐ง ๐จ๐ฎ๐ซ ๐๐ฉ๐๐ง-๐๐จ๐ฎ๐ซ๐๐ ๐๐ ๐๐จ๐จ๐ค๐๐จ๐จ๐ค: ๐๐ฉ๐ง๐ช๐๐ฉ๐ช๐ง๐๐ ๐๐๐ฃ๐๐ง๐๐ฉ๐๐ค๐ฃ! โจ
Many use LLM use cases involve generating outputs with a specific structure.
โก๏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
}
```
๐ค How to force your LLM to generate such a structured output?
๐๏ธ ๐๐ผ๐ป๐๐๐ฟ๐ฎ๐ถ๐ป๐ฒ๐ฑ ๐ฑ๐ฒ๐ฐ๐ผ๐ฑ๐ถ๐ป๐ด is a great technique to generate structured output: you can specify a grammar (=set of rules) that the output should follow, and ๐ฐ๐ผ๐ป๐๐๐ฟ๐ฎ๐ถ๐ป๐ฒ๐ฑ ๐ฑ๐ฒ๐ฐ๐ผ๐ฑ๐ถ๐ป๐ด ๐๐ต๐ฒ๐ป ๐ณ๐ผ๐ฟ๐ฐ๐ฒ๐ ๐๐ต๐ฒ ๐ฑ๐ฒ๐ฐ๐ผ๐ฑ๐ฒ๐ฟ ๐๐ผ ๐ผ๐ป๐น๐ ๐ฝ๐ถ๐ฐ๐ธ ๐๐ผ๐ธ๐ฒ๐ป๐ ๐๐ต๐ฎ๐ ๐ฟ๐ฒ๐๐ฝ๐ฒ๐ฐ๐ ๐๐ผ๐๐ฟ ๐ด๐ฟ๐ฎ๐บ๐บ๐ฎ๐ฟ.
I've created a guide to show you how to use it, both via our Inference API and locally using ๐ฐ๐ถ๐ต๐ญ๐ช๐ฏ๐ฆ๐ด!
๐ Read it here: https://huggingface.co/learn/cookbook/structured_generation
Thank you @stevhliu for your great help in improving it! | {
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"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. ",
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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.
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(Prompt: Please create me a gloomy forest showing the moon it is a very dark hollow place)
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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 ๐ฅณ
https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct is still the king of the leaderboard ๐ with a 3.46 points difference compared to its successor https://huggingface.co/CohereForAI/c4ai-command-r-plus who took the 2nd place ๐ฅ 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 ๐ฅ- (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 ๐
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] | ๐ Meet MergeUI - an All-in-one UI for Exploring Merged LLMs on Hugging Face ๐ค!
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:
- ๐งฌ Try MergeUI - https://naskio-mergeui.hf.space
- ๐จโ๐ป Source Code - https://github.com/naskio/mergeui
Love this project? boost it on GitHub and share it with your network.
#merge #mergekit #leaderboard
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"value": "โก๏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.",
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] | A non-Instruct LLM assistant is mostly useless. ๐ง
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.
โก๏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 ๐ https://www.adaptive-ml.com/post/from-zero-to-ppo | {
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Important context for current AI safety discussions and regulation debates.
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] | New adapt.s for dev ๐
Hosted -> https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC
โจTeen Outfit: https://huggingface.co/prithivMLmods/Teen-Outfit
โจDark Pink: https://huggingface.co/prithivMLmods/Dark-Thing-Flux-LoRA
โจShadow Projection: https://huggingface.co/prithivMLmods/Shadow-Projection-Flux-LoRA
โจAbstract Cartoon: https://huggingface.co/prithivMLmods/Abstract-Cartoon-Flux-LoRA
โจStreet Bokeh: https://huggingface.co/prithivMLmods/Street-Bokeh-Flux-LoRA
โจFine Detailed: https://huggingface.co/prithivMLmods/Flux-Realism-FineDetailed
โจBold Shadows: https://huggingface.co/prithivMLmods/Bold-Shadows-Flux-LoRA
โจYellow Laser: https://huggingface.co/prithivMLmods/Yellow-Laser-Flux-LoRA
------------
๐LoRA Collection: https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
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[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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๐ 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.
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] | ๐ต 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
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- Cross-modal analysis (text-to-audio relationships)
- User engagement studies
- Audio classification tasks
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Drop-in replacement to GPT-4o as a coding assistant on Cursor or for Artifacts!",
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โจ Completes the previous two Qwen 2.5 Coder release with 4 new size: 0.5B, 3B, 14B, 32B
๐ Support long context up to 128K (for the 14B and 32B models)
โ
Drop-in replacement to GPT-4o as a coding assistant on Cursor or for Artifacts!
๐ค 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!
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๐ ๐๐ฃ๐ง ๐บ๐ผ๐ฑ๐ฒ๐น๐.",
"raw": "๐๐ฟ๐ฒ ๐๐ฐ๐ฎ๐น๐ถ๐ป๐ด ๐น๐ฎ๐๐ ๐ผ๐๐ฒ๐ฟ? ๐ ๐ฟ๐ฒ๐ฝ๐ผ๐ฟ๐ ๐ณ๐ฟ๐ผ๐บ ๐๐ต๐ฒ ๐๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป ๐ฎ๐ป๐ป๐ผ๐๐ป๐ฐ๐ฒ๐ฑ ๐๐ต๐ฎ๐ ๐ข๐ฝ๐ฒ๐ป๐๐ ๐ถ๐ ๐๐ฒ๐ฒ๐ถ๐ป๐ด ๐ฑ๐ถ๐บ๐ถ๐ป๐ถ๐๐ต๐ถ๐ป๐ด ๐ฟ๐ฒ๐๐๐ฟ๐ป๐ ๐ณ๐ฟ๐ผ๐บ ๐๐ฐ๐ฎ๐น๐ถ๐ป๐ด ๐๐ฝ ๐๐ต๐ฒ ๐ป๐ฒ๐
๐ ๐๐ฃ๐ง ๐บ๐ผ๐ฑ๐ฒ๐น๐.",
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"value": "๐ 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": "๐ 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.",
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"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).",
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"value": "โก๏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": "โก๏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.",
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"value": "๐ค So, what about these reports of scaling laws slowing down?",
"raw": "๐ค So, what about these reports of scaling laws slowing down?",
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"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",
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"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. ",
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] | ๐๐ฟ๐ฒ ๐๐ฐ๐ฎ๐น๐ถ๐ป๐ด ๐น๐ฎ๐๐ ๐ผ๐๐ฒ๐ฟ? ๐ ๐ฟ๐ฒ๐ฝ๐ผ๐ฟ๐ ๐ณ๐ฟ๐ผ๐บ ๐๐ต๐ฒ ๐๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป ๐ฎ๐ป๐ป๐ผ๐๐ป๐ฐ๐ฒ๐ฑ ๐๐ต๐ฎ๐ ๐ข๐ฝ๐ฒ๐ป๐๐ ๐ถ๐ ๐๐ฒ๐ฒ๐ถ๐ป๐ด ๐ฑ๐ถ๐บ๐ถ๐ป๐ถ๐๐ต๐ถ๐ป๐ด ๐ฟ๐ฒ๐๐๐ฟ๐ป๐ ๐ณ๐ฟ๐ผ๐บ ๐๐ฐ๐ฎ๐น๐ถ๐ป๐ด ๐๐ฝ ๐๐ต๐ฒ ๐ป๐ฒ๐
๐ ๐๐ฃ๐ง ๐บ๐ผ๐ฑ๐ฒ๐น๐.
๐ 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).
โก๏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.
๐ค 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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https://huggingface.co/papers/2405.12981
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] | mlx port of karpathyโs minbpe ๐ค
Minimal (byte-level) Byte Pair Encoding tokenizer. Algorithmically follows along the GPT2 tokenizer.
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Size Consistency: While Krakenโs size increases with more Experts, Kraken-LoRA remains as compact as the base model (e.g., 8b if you use Meta-Llama3-8b-Instruct).",
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Size Consistency: While Krakenโs size increases with more Experts, Kraken-LoRA remains as compact as the base model (e.g., 8b if you use Meta-Llama3-8b-Instruct).",
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VRAM Efficiency: Kraken-LoRA is highly VRAM efficient, maintaining the power of all experts without the bloat.",
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VRAM Efficiency: Kraken-LoRA is highly VRAM efficient, maintaining the power of all experts without the bloat.",
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Dynamic Adaptation: LoRA adapters are applied dynamically at runtime, following the routing process.",
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Dynamic Adaptation: LoRA adapters are applied dynamically at runtime, following the routing process.",
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High Efficiency: Enjoy increased efficiency without compromising performance, as long as the LoRA adapters match the base model.",
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High Efficiency: Enjoy increased efficiency without compromising performance, as long as the LoRA adapters match the base model.",
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] | 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!
๐ Whatโs the big deal?
โ
Size Consistency: While Krakenโs size increases with more Experts, Kraken-LoRA remains as compact as the base model (e.g., 8b if you use Meta-Llama3-8b-Instruct).
โ
VRAM Efficiency: Kraken-LoRA is highly VRAM efficient, maintaining the power of all experts without the bloat.
โ
Dynamic Adaptation: LoRA adapters are applied dynamically at runtime, following the routing process.
โ
High Efficiency: Enjoy increased efficiency without compromising performance, as long as the LoRA adapters match the base model.
๐ก 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! ๐ | {
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Impressive to see Cohere for AI's new Aya model multilingual capabilities.
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> Multilingual (23 languages), beats Mistral 7B and Llama3 8B in preferenceโopen weights.
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๐ **Multilingual Mastery**: Supporting 23 languages, including Arabic!
๐ **Top Performer**: Outperforms Mistral 7B and Llama3 8B in user preference.
๐ **Open Weights**: Access open weights for your research and projects.
๐ **License**: CC-BY-NC with adherence to C4AI's Acceptable Use Policy.
๐ผ **Developed by**: Cohere For AI and Cohere.
Check out Aya 23 on Hugging Face , link is in comments
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"value": " have introduced Chameleon ๐ฆ (who names these things? ๐คทโโ๏ธ)",
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Chameleon is an AI model that can work with multiple types of data, like text and images, all at once. ๐ผ๏ธ๐
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! ๐ซ๐
Still, here is the technical stuff:
๐ Challenges with Current Systems:
๐ Fragmentation: Current multimodal models are often specialized for either text or image tasks, lacking unified approaches.
๐ Scalability: Existing systems struggle with scaling to handle complex, mixed-modal tasks without significant performance degradation.
๐ Alignment: Aligning textual and visual modalities remains a technical challenge, often requiring separate processing pipelines.
๐ Objective:
๐ฏ Unified Modeling: Develop a single model capable of handling various multimodal tasks (text generation, image generation, image captioning, visual question answering) seamlessly.
๐ How It's Done ๐
Early-Fusion Architecture ๐ง : Utilizes an early-fusion token-based approach to integrate text and image data from the beginning.
Stable Training ๐ช: Implements a tailored alignment recipe and specific architectural parameterization to ensure stability in mixed-modal settings.
Broad Evaluation ๐: Assesses the model across various tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed-modal generation.
๐ Results: (Fun fact they mention Llava-1.5 in comparison but never really share the results)
๐ Performance: Chameleon achieves state-of-the-art results in image captioning and outperforms models like Llama-2 in text-only tasks.
โ๏ธ Competitiveness: It shows competitive performance with models such as Mixtral 8x7B and Gemini-Pro.
๐ฉโโ๏ธ Human Judgments: Matches or exceeds the performance of larger models, including Gemini Pro and GPT-4V
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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.
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] | ๐๐ญ๐ New Research Alert - Gaussian Head & Shoulders (Avatars Collection)! ๐๐ญ๐
๐ Title: Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with Anchor Gaussian Guided Texture Warping ๐
๐ 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.
๐ฅ Authors: Tianhao Wu et al.
๐ Paper: https://huggingface.co/papers/2405.12069
๐ Github Page: https://gaussian-head-shoulders.netlify.app
๐ More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
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"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: ",
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] | 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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449324773772907 | [
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"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.",
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] | 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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"value": " ControlNet with Diffusers completely.",
"raw": " ControlNet with Diffusers completely.",
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"raw": "Super resolution version: ",
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] | 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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"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.",
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"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.",
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] | 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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"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: ",
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"raw": "This is a space that lets you predict the 3D structure of proteins from their amino-acidic sequences, with the protein folding model ",
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"value": ": using this space is the perfect quick-start to become a Protein Scientist! (or maybe not, who knows...๐ค)",
"raw": ": using this space is the perfect quick-start to become a Protein Scientist! (or maybe not, who knows...๐ค)",
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"value": "In the meantime, if you are curious about what's going on with AlphaFold3 and want something Biologist๐ฌ/Computer Scientist๐ป-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 Biologist๐ฌ/Computer Scientist๐ป-friendly, you can also check out the latest community blog post I wrote: ",
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] | Hi HF Community!๐ค
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...๐ค)
In the meantime, if you are curious about what's going on with AlphaFold3 and want something Biologist๐ฌ/Computer Scientist๐ป-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 ๐
Have fun and enjoy open-source science!๐งฌ | {
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] | 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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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
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] | 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
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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+",
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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+",
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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.",
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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.",
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Adaptability: Enhanced input formatting supports the modelโs adaptability to diverse conversational contexts.",
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Adaptability: Enhanced input formatting supports the modelโs adaptability to diverse conversational contexts.",
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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.",
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Open Source Pipeline: Weโre sharing the entire pipeline, including router creation, training, architecture setup, and Kraken inference, on JupyterNotebooks: ",
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Open Source Pipeline: Weโre sharing the entire pipeline, including router creation, training, architecture setup, and Kraken inference, on JupyterNotebooks: ",
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] | 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? ๐
โ
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+
โ
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.
โ
Adaptability: Enhanced input formatting supports the modelโs adaptability to diverse conversational contexts.
โ
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.
โ
Open Source Pipeline: Weโre 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 ๐คฉ
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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