Fancy Stateful Metaflow Service + UI on Google Colab ?
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Aurรฉlien-Morgan CLAUDON
Aurelien-Morgan
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Build a collection for the trending demos recently released by the Chinese community ๐ From Qwen2.5 Turbo to FishAgent, see what these models can really do ๐ฅ
zh-ai-community/trending-demo-673b6ca2416a3b3c9d3bf8f1
zh-ai-community/trending-demo-673b6ca2416a3b3c9d3bf8f1
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โ ๏ธ People selling AI chatbots for websites hate us.
Add an open source chat assistant on your website in 5 minutes: https://github.com/phospho-app/ai-chat-bubble
How does it work ?
- You give an URL
- The AI assistant crawls the website content and embed it
- Add it to your frontend in one line of code
- People on your website can ask the assistant questions
Powered by BAAI/bge-small-en-v1.5 and Mistral AI
Add an open source chat assistant on your website in 5 minutes: https://github.com/phospho-app/ai-chat-bubble
How does it work ?
- You give an URL
- The AI assistant crawls the website content and embed it
- Add it to your frontend in one line of code
- People on your website can ask the assistant questions
Powered by BAAI/bge-small-en-v1.5 and Mistral AI
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๐ ๐ฒ๐๐ฎ ๐๐ฒ๐ฎ๐บ ๐ท๐๐๐ ๐ฑ๐ฟ๐ผ๐ฝ๐ฝ๐ฒ๐ฑ ๐๐ต๐ฒ ๐ณ๐ถ๐ฟ๐๐ ๐ช๐ฎ๐๐ฒ๐ฟ๐บ๐ฎ๐ฟ๐ธ๐ถ๐ป๐ด ๐บ๐ผ๐ฑ๐ฒ๐น ๐๐ต๐ฎ๐ ๐ป๐ผ๐ ๐ฒ๐ฑ๐ถ๐ ๐ฐ๐ฎ๐ป ๐ฏ๐ฟ๐ฒ๐ฎ๐ธ!๐ก๏ธ
๐ค Ever heard of watermarking? It's a technique that allows you to mark in an image its original source. It's our best shield against AI-generated deepfakes, or content stolen from artists! ๐จ
๐ญ Watermarking systems are actually a pair of models: a watermark embedder that applies the watermark on the image, and its corresponding decoder that should detect the original watermark.
โ But current methods were very limited: they can only apply and detect the watermark on your image as a whole. So, if you're an attacker it's easy to break: just crop it! add text on top! or whatever, really, anything would work to break the watermark.
A team of researchers at Meta was not happy with this. ๐ค
So to withstand real-world attacks, they decided to make a watermarking model that would also work on any sub-part of the image. It's a real paradigm shift: they consider watermarking not as an image classification task, but as an image segmentation task!
๐๏ธ ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ
โธ The "Embedder" (a variational autoencoder + embedder, 1.1M parameters in total) encodes a n-bit message into a watermark signal that is added to the original image
โธ [Only during training] The "Augmenter" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point.
โธ The "Extractor" (a vision transformer, or ViT, with 96M parameters) then re-extracts the message from the distorted image, by predicting a (1+n) vector per pixel to predict the watermarked parts and decode corresponding messages.
The performance blows existing models out of the water, they even created new tasks (segmentation-related) just to grok them!
Gerat work @pierrefdz and @tomsander1998 !
Paper here ๐ Watermark Anything with Localized Messages (2411.07231)
๐ค Ever heard of watermarking? It's a technique that allows you to mark in an image its original source. It's our best shield against AI-generated deepfakes, or content stolen from artists! ๐จ
๐ญ Watermarking systems are actually a pair of models: a watermark embedder that applies the watermark on the image, and its corresponding decoder that should detect the original watermark.
โ But current methods were very limited: they can only apply and detect the watermark on your image as a whole. So, if you're an attacker it's easy to break: just crop it! add text on top! or whatever, really, anything would work to break the watermark.
A team of researchers at Meta was not happy with this. ๐ค
So to withstand real-world attacks, they decided to make a watermarking model that would also work on any sub-part of the image. It's a real paradigm shift: they consider watermarking not as an image classification task, but as an image segmentation task!
๐๏ธ ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ
โธ The "Embedder" (a variational autoencoder + embedder, 1.1M parameters in total) encodes a n-bit message into a watermark signal that is added to the original image
โธ [Only during training] The "Augmenter" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point.
โธ The "Extractor" (a vision transformer, or ViT, with 96M parameters) then re-extracts the message from the distorted image, by predicting a (1+n) vector per pixel to predict the watermarked parts and decode corresponding messages.
The performance blows existing models out of the water, they even created new tasks (segmentation-related) just to grok them!
Gerat work @pierrefdz and @tomsander1998 !
Paper here ๐ Watermark Anything with Localized Messages (2411.07231)
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A while ago I started experimenting with compiling the Python interpreter to WASM.
To build a secure, fast, and lightweight sandbox for code execution โ ideal for running LLM-generated Python code.
- Send code simply as a POST request
- 1-2ms startup times
Hack away:
https://github.com/ErikKaum/runner
To build a secure, fast, and lightweight sandbox for code execution โ ideal for running LLM-generated Python code.
- Send code simply as a POST request
- 1-2ms startup times
Hack away:
https://github.com/ErikKaum/runner
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๐ค transformers pipelines now support vision language models for easy local inference ๐ซฐ๐ป
h/t @yonigozlan for shipping this ๐ฉ๐
you can also use inference API to infer hosted vision LMs (via Python, JS and cURL) https://huggingface.co/docs/api-inference/en/tasks/image-text-to-text
h/t @yonigozlan for shipping this ๐ฉ๐
you can also use inference API to infer hosted vision LMs (via Python, JS and cURL) https://huggingface.co/docs/api-inference/en/tasks/image-text-to-text
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I was curious to see what people post here on HF so I created a dataset with all HF Posts:
maxiw/hf-posts
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions
Top 5 Most Used Reactions:
-------------------------
โค๏ธ: 7048 times
๐ฅ: 5921 times
๐: 4856 times
๐: 2549 times
๐ค: 2065 times
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions
Top 5 Most Used Reactions:
-------------------------
โค๏ธ: 7048 times
๐ฅ: 5921 times
๐: 4856 times
๐: 2549 times
๐ค: 2065 times
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Native tensor parallel has landed in transformers!!! https://github.com/huggingface/transformers/pull/34184 thanks a lot to the torch team for their support!
Contributions are welcome to support more models! ๐ฅ
Contributions are welcome to support more models! ๐ฅ
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Letโs dive into the exciting releases from the Chinese community last week ๐ฅ๐
More details ๐ https://huggingface.co/zh-ai-community
Code model:
โจQwen 2.5 coder by Alibaba Qwen
Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f
โจOpenCoder by InflyAI - Fully open code model๐
infly/opencoder-672cec44bbb86c39910fb55e
Image model:
โจHunyuan3D-1.0 by Tencent
tencent/Hunyuan3D-1
MLLM:
โจJanusFlow by DeepSeek
deepseek-ai/JanusFlow-1.3B
deepseek-ai/JanusFlow-1.3B
โจMono-InternVL-2B by OpenGVlab
OpenGVLab/Mono-InternVL-2B
Video model:
โจCogVideoX 1.5 by ChatGLM
THUDM/CogVideoX1.5-5B-SAT
Audio model:
โจFish Agent by FishAudio
fishaudio/fish-agent-v0.1-3b
Dataset:
โจOPI dataset by BAAIBeijing
BAAI/OPI
More details ๐ https://huggingface.co/zh-ai-community
Code model:
โจQwen 2.5 coder by Alibaba Qwen
Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f
โจOpenCoder by InflyAI - Fully open code model๐
infly/opencoder-672cec44bbb86c39910fb55e
Image model:
โจHunyuan3D-1.0 by Tencent
tencent/Hunyuan3D-1
MLLM:
โจJanusFlow by DeepSeek
deepseek-ai/JanusFlow-1.3B
deepseek-ai/JanusFlow-1.3B
โจMono-InternVL-2B by OpenGVlab
OpenGVLab/Mono-InternVL-2B
Video model:
โจCogVideoX 1.5 by ChatGLM
THUDM/CogVideoX1.5-5B-SAT
Audio model:
โจFish Agent by FishAudio
fishaudio/fish-agent-v0.1-3b
Dataset:
โจOPI dataset by BAAIBeijing
BAAI/OPI
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I just shipped
I'll have to focus on another project for the next couple weeks but, anyone feel free to open issues on the GitHub repo and discuss any interest you'd have there if you will (please?) !
In the meantime, you may enjoy retrying this :
https://huggingface.co/blog/Aurelien-Morgan/stateful-metaflow-on-colab
retrain-pipelines 0.1.1 today. The doc is also pimped compared to previous release. That was clearly not mature then.I'll have to focus on another project for the next couple weeks but, anyone feel free to open issues on the GitHub repo and discuss any interest you'd have there if you will (please?) !
In the meantime, you may enjoy retrying this :
https://huggingface.co/blog/Aurelien-Morgan/stateful-metaflow-on-colab
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