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Transformers.js v3: WebGPU support, new models & tasks, and more…
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Reacted to
Xenova's
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5 months ago
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5959
Florence-2, the new vision foundation model by Microsoft, can now run 100% locally in your browser on WebGPU, thanks to Transformers.js! 🤗🤯
It supports tasks like image captioning, optical character recognition, object detection, and many more! 😍 WOW!
- Demo: Xenova/florence2-webgpu
- Models: https://huggingface.co/models?library=transformers.js&other=florence2
- Source code: https://github.com/xenova/transformers.js/tree/v3/examples/florence2-webgpu
It supports tasks like image captioning, optical character recognition, object detection, and many more! 😍 WOW!
- Demo: Xenova/florence2-webgpu
- Models: https://huggingface.co/models?library=transformers.js&other=florence2
- Source code: https://github.com/xenova/transformers.js/tree/v3/examples/florence2-webgpu
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Fine-tune Florence-2 on any task 🔥
Today we release a notebook and a walkthrough blog on fine-tuning Florence-2 on DocVQA dataset @andito @SkalskiP
Blog: https://huggingface.co/blog 📕
Notebook: https://colab.research.google.com/drive/1hKDrJ5AH_o7I95PtZ9__VlCTNAo1Gjpf?usp=sharing 📖
Florence-2 is a great vision-language model thanks to it's massive dataset and small size!
This model requires conditioning through task prefixes and it's not as generalist, requiring fine-tuning on a new task, such as DocVQA 📝
We have fine-tuned the model on A100 (and one can also use a smaller GPU with smaller batch size) and saw that model picks up new tasks 🥹
See below how it looks like before and after FT 🤩
Play with the demo here andito/Florence-2-DocVQA 🏄♀️
Today we release a notebook and a walkthrough blog on fine-tuning Florence-2 on DocVQA dataset @andito @SkalskiP
Blog: https://huggingface.co/blog 📕
Notebook: https://colab.research.google.com/drive/1hKDrJ5AH_o7I95PtZ9__VlCTNAo1Gjpf?usp=sharing 📖
Florence-2 is a great vision-language model thanks to it's massive dataset and small size!
This model requires conditioning through task prefixes and it's not as generalist, requiring fine-tuning on a new task, such as DocVQA 📝
We have fine-tuned the model on A100 (and one can also use a smaller GPU with smaller batch size) and saw that model picks up new tasks 🥹
See below how it looks like before and after FT 🤩
Play with the demo here andito/Florence-2-DocVQA 🏄♀️
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Florence-2 is a new vision foundation model capable of a wide variety of tasks 🤯
Demo 👉🏻 gokaygokay/Florence-2
Collection 👉🏻 microsoft/florence-6669f44df0d87d9c3bfb76de
This model can handle tasks that vary from OCR to semantic segmentation.
The difference from previous models is that the authors have compiled a dataset consisting of 126M images with 5.4B annotations labelled with their own data engine pseudolabelled by smaller specialized models and APIs.
The model has a similar architecture to previous models: an image encoder and a multimodality encoder with a text decoder. The authors have compiled the multitask dataset with prompts for each task.
You can also fine-tune this model on any task of choice. The authors also released different results on downstream tasks and reported their results when un/freezing the vision encoder 🤓📉
They have released fine-tuned models too, you can find them in the collection above 🤗
Demo 👉🏻 gokaygokay/Florence-2
Collection 👉🏻 microsoft/florence-6669f44df0d87d9c3bfb76de
This model can handle tasks that vary from OCR to semantic segmentation.
The difference from previous models is that the authors have compiled a dataset consisting of 126M images with 5.4B annotations labelled with their own data engine pseudolabelled by smaller specialized models and APIs.
The model has a similar architecture to previous models: an image encoder and a multimodality encoder with a text decoder. The authors have compiled the multitask dataset with prompts for each task.
You can also fine-tune this model on any task of choice. The authors also released different results on downstream tasks and reported their results when un/freezing the vision encoder 🤓📉
They have released fine-tuned models too, you can find them in the collection above 🤗
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#2 opened 7 months ago
by
BoscoTheDog
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just landed at Hugging Face Hub: community-led computer vision course 📖🤍
learn from fundamentals to details of the bleeding edge vision transformers!
learn from fundamentals to details of the bleeding edge vision transformers!
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Released a new version of
vikhyatk/moondream2 today! Primarily focused on improving OCR and captioning (e.g. "Describe this image", "Describe this image in one sentence"), but also seeing general improvement across all benchmarks.
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⚡ WebGPU Benchmark Results (183.84x speedup)
#50 opened 9 months ago
by
omaryshchenko
omaryshchenko
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9 months ago
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Introducing the 🤗 Transformers.js WebGPU Embedding Benchmark! ⚡️
👉 Xenova/webgpu-embedding-benchmark 👈
On my device, I was able to achieve a 64.04x speedup over WASM! 🤯 How much does WebGPU speed up ML models running locally in your browser? Try it out and share your results! 🚀
👉 Xenova/webgpu-embedding-benchmark 👈
On my device, I was able to achieve a 64.04x speedup over WASM! 🤯 How much does WebGPU speed up ML models running locally in your browser? Try it out and share your results! 🚀
- 3 replies
⚡ WebGPU Benchmark Results (32.32x speedup)
#38 opened 9 months ago
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omaryshchenko
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⭐ Today we’re releasing The Stack v2 & StarCoder2: a series of 3B, 7B & 15B code generation models trained on 3.3 to 4.5 trillion tokens of code:
- StarCoder2-15B matches or outperforms CodeLlama 34B, and approaches DeepSeek-33B on multiple benchmarks.
- StarCoder2-3B outperforms StarCoderBase-15B and similar sized models.
- The Stack v2 a 4x larger dataset than the Stack v1, resulting in 900B unique code tokens 🚀
As always, we released everything from models and datasets to curation code. Enjoy!
🔗 StarCoder2 collection: bigcode/starcoder2-65de6da6e87db3383572be1a
🔗 Paper: https://drive.google.com/file/d/17iGn3c-sYNiLyRSY-A85QOzgzGnGiVI3/view
🔗 BlogPost: https://huggingface.co/blog/starcoder2
🔗 Code Leaderboard: bigcode/bigcode-models-leaderboard
- StarCoder2-15B matches or outperforms CodeLlama 34B, and approaches DeepSeek-33B on multiple benchmarks.
- StarCoder2-3B outperforms StarCoderBase-15B and similar sized models.
- The Stack v2 a 4x larger dataset than the Stack v1, resulting in 900B unique code tokens 🚀
As always, we released everything from models and datasets to curation code. Enjoy!
🔗 StarCoder2 collection: bigcode/starcoder2-65de6da6e87db3383572be1a
🔗 Paper: https://drive.google.com/file/d/17iGn3c-sYNiLyRSY-A85QOzgzGnGiVI3/view
🔗 BlogPost: https://huggingface.co/blog/starcoder2
🔗 Code Leaderboard: bigcode/bigcode-models-leaderboard