Ethics and Society Newsletter #6: Building Better AI: The Importance of Data Quality
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Brigitte Tousignant
BrigitteTousi
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davidberenstein1957's
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tldr; Parquet is awesome, DuckDB too!
Datasets on the Hugging Face Hub rely on parquet files. We can interact with these files using DuckDB as a fast in-memory database system. One of DuckDBโs features is vector similarity search which can be used with or without an index.
blog:
https://huggingface.co/learn/cookbook/vector_search_with_hub_as_backend
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BrigitteTousi's activity
reacted to
fdaudens's
post with ๐
about 6 hours ago
reacted to
davidberenstein1957's
post with ๐
about 6 hours ago
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702
tldr; Parquet is awesome, DuckDB too!
Datasets on the Hugging Face Hub rely on parquet files. We can interact with these files using DuckDB as a fast in-memory database system. One of DuckDBโs features is vector similarity search which can be used with or without an index.
blog:
https://huggingface.co/learn/cookbook/vector_search_with_hub_as_backend
Datasets on the Hugging Face Hub rely on parquet files. We can interact with these files using DuckDB as a fast in-memory database system. One of DuckDBโs features is vector similarity search which can be used with or without an index.
blog:
https://huggingface.co/learn/cookbook/vector_search_with_hub_as_backend
reacted to
fdaudens's
post with ๐ฅ
about 6 hours ago
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841
๐ฏ Kokoro TTS just hit v1.0! ๐
Small but mighty: 82M parameters, runs locally, speaks multiple languages. The best part? It's Apache 2.0 licensed!
This could unlock so many possibilities โจ
Check it out: hexgrad/Kokoro-82M
Small but mighty: 82M parameters, runs locally, speaks multiple languages. The best part? It's Apache 2.0 licensed!
This could unlock so many possibilities โจ
Check it out: hexgrad/Kokoro-82M
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pagezyhf's
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about 6 hours ago
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We published https://huggingface.co/blog/deepseek-r1-aws!
If you are using AWS, give a read. It is a running document to showcase how to deploy and fine-tune DeepSeek R1 models with Hugging Face on AWS.
We're working hard to enable all the scenarios, whether you want to deploy to Inference Endpoints, Sagemaker or EC2; with GPUs or with Trainium & Inferentia.
We have full support for the distilled models, DeepSeek-R1 support is coming soon!! I'll keep you posted.
Cheers
If you are using AWS, give a read. It is a running document to showcase how to deploy and fine-tune DeepSeek R1 models with Hugging Face on AWS.
We're working hard to enable all the scenarios, whether you want to deploy to Inference Endpoints, Sagemaker or EC2; with GPUs or with Trainium & Inferentia.
We have full support for the distilled models, DeepSeek-R1 support is coming soon!! I'll keep you posted.
Cheers
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AdinaY's
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1 day ago
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2522
Itโs not just a flood of model releases, papers are dropping just as fast ๐
Here are the 10 most upvoted papers from the Chinese community:
๐ zh-ai-community/2025-january-papers-679933cbf0f3ced11f5a168a
Here are the 10 most upvoted papers from the Chinese community:
๐ zh-ai-community/2025-january-papers-679933cbf0f3ced11f5a168a
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davanstrien's
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1 day ago
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Why choose between strong LLM reasoning and efficient models?
Use DeepSeek to generate high-quality training data, then distil that knowledge into ModernBERT answerdotai/ModernBERT-base for fast, efficient classification.
Blog post: https://danielvanstrien.xyz/posts/2025/deepseek/distil-deepseek-modernbert.html
Use DeepSeek to generate high-quality training data, then distil that knowledge into ModernBERT answerdotai/ModernBERT-base for fast, efficient classification.
Blog post: https://danielvanstrien.xyz/posts/2025/deepseek/distil-deepseek-modernbert.html
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m-ric's
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2 days ago
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๐ง๐ต๐ฒ ๐๐๐ฏ ๐๐ฒ๐น๐ฐ๐ผ๐บ๐ฒ๐ ๐ฒ๐
๐๐ฒ๐ฟ๐ป๐ฎ๐น ๐ถ๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ ๐ฝ๐ฟ๐ผ๐๐ถ๐ฑ๐ฒ๐ฟ๐!
โ Hosting our own inference was not enough: now the Hub 4 new inference providers: fal, Replicate, SambaNova Systems, & Together AI.
Check model cards on the Hub: you can now, in 1 click, use inference from various providers (cf video demo)
Their inference can also be used through our Inference API client. There, you can use either your custom provider key, or your HF token, then billing will be handled directly on your HF account, as a way to centralize all expenses.
๐ธ Also, PRO users get 2$ inference credits per month!
Read more in the announcement ๐ https://huggingface.co/blog/inference-providers
โ Hosting our own inference was not enough: now the Hub 4 new inference providers: fal, Replicate, SambaNova Systems, & Together AI.
Check model cards on the Hub: you can now, in 1 click, use inference from various providers (cf video demo)
Their inference can also be used through our Inference API client. There, you can use either your custom provider key, or your HF token, then billing will be handled directly on your HF account, as a way to centralize all expenses.
๐ธ Also, PRO users get 2$ inference credits per month!
Read more in the announcement ๐ https://huggingface.co/blog/inference-providers
- 1 reply
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odellus's
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2 days ago
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Tired: shitposting on bsky
Wired: shitposting on hf
Wired: shitposting on hf
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chansung's
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2 days ago
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1837
Simple summary on DeepSeek AI's Janus-Pro: A fresh take on multimodal AI!
It builds on its predecessor, Janus, by tweaking the training methodology rather than the model architecture. The result? Improved performance in understanding and generating multimodal data.
Janus-Pro uses a three-stage training strategy, similar to Janus, but with key modifications:
โฆ Stage 1 & 2: Focus on separate training for specific objectives, rather than mixing data.
โฆ Stage 3: Fine-tuning with a careful balance of multimodal data.
Benchmarks show Janus-Pro holds its own against specialized models like TokenFlow XL and MetaMorph, and other multimodal models like SD3 Medium and DALL-E 3.
The main limitation? Low image resolution (384x384). However, this seems like a strategic choice to focus on establishing a solid "recipe" for multimodal models. Future work will likely leverage this recipe and increased computing power to achieve higher resolutions.
It builds on its predecessor, Janus, by tweaking the training methodology rather than the model architecture. The result? Improved performance in understanding and generating multimodal data.
Janus-Pro uses a three-stage training strategy, similar to Janus, but with key modifications:
โฆ Stage 1 & 2: Focus on separate training for specific objectives, rather than mixing data.
โฆ Stage 3: Fine-tuning with a careful balance of multimodal data.
Benchmarks show Janus-Pro holds its own against specialized models like TokenFlow XL and MetaMorph, and other multimodal models like SD3 Medium and DALL-E 3.
The main limitation? Low image resolution (384x384). However, this seems like a strategic choice to focus on establishing a solid "recipe" for multimodal models. Future work will likely leverage this recipe and increased computing power to achieve higher resolutions.
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fdaudens's
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2 days ago
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๐ The open source community is unstoppable: 4M total downloads for DeepSeek models on Hugging Face, with 3.2M coming from the +600 models created by the community.
That's 30% more than yesterday!
That's 30% more than yesterday!
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cfahlgren1's
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2 days ago
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If you haven't seen yet, we just released Inference Providers ๐
> 4 new serverless inference providers on the Hub ๐คฏ
> Use your HF API key or personal key with all providers ๐
> Chat with Deepseek R1, V3, and more on HF Hub ๐
> We support Sambanova, TogetherAI, Replicate, and Fal.ai ๐ช
Best of all, we don't charge any markup on top of the provider ๐ซฐ Have you tried it out yet? HF Pro accounts get $2 of free usage for the provider inference.
> 4 new serverless inference providers on the Hub ๐คฏ
> Use your HF API key or personal key with all providers ๐
> Chat with Deepseek R1, V3, and more on HF Hub ๐
> We support Sambanova, TogetherAI, Replicate, and Fal.ai ๐ช
Best of all, we don't charge any markup on top of the provider ๐ซฐ Have you tried it out yet? HF Pro accounts get $2 of free usage for the provider inference.
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2 days ago
Article
Open-R1: a fully open reproduction of DeepSeek-R1
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