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5781
5,000 new repos (models, datasets, spaces) are created EVERY DAY on HF now. The community is amazing!
[solved]
1
#3 opened 7 months ago
by
Qualzz20
[solved]
1
#3 opened 7 months ago
by
Qualzz20
reacted to
reach-vb's
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6 months ago
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5270
Yet another rewarding week in Open Source AI:
1. Google dropped Gemma 27B & 9B - The best open (commercially permissive) LLM out there, according to LYMSYS.
google/gemma-2-release-667d6600fd5220e7b967f315
2. Mars5 TTS - Text to Speech with insane prosodies control & voice cloning.
CAMB-AI/MARS5-TTS
3. Meta shipped LLM Compiler - beats GPT 4 on code optimisation and compiler reasoning.
facebook/llm-compiler-667c5b05557fe99a9edd25cb
4. Arcee-Spark - Qwen2 7B (w/ merging) fine-tuned further to beat GPT 3.5 on MT Bench.
arcee-ai/Arcee-Spark
5. Gemini Nano out in the wild in Chrome - On device LLM with just 2 lines of code (fully offline)
6. Fal released a fully Open Source GAN based Super-Resolution model (with second version already cooking)
fal/AuraSR
7. NYU release Cambrian 1 - Vision Multimodal LLM that beats pretty much all other closed source competition 8-34B model size
https://huggingface.co/nyu-visionx
And.. much more like Open LLM Leaderboard got a major update, LYMSYS released Chat Vision Arena, OpenAI released a paper on CriticGPT!
What a lovely week, can’t wait for the next to see what the community is up to! Put it down in comments if I missed something 🔥
1. Google dropped Gemma 27B & 9B - The best open (commercially permissive) LLM out there, according to LYMSYS.
google/gemma-2-release-667d6600fd5220e7b967f315
2. Mars5 TTS - Text to Speech with insane prosodies control & voice cloning.
CAMB-AI/MARS5-TTS
3. Meta shipped LLM Compiler - beats GPT 4 on code optimisation and compiler reasoning.
facebook/llm-compiler-667c5b05557fe99a9edd25cb
4. Arcee-Spark - Qwen2 7B (w/ merging) fine-tuned further to beat GPT 3.5 on MT Bench.
arcee-ai/Arcee-Spark
5. Gemini Nano out in the wild in Chrome - On device LLM with just 2 lines of code (fully offline)
6. Fal released a fully Open Source GAN based Super-Resolution model (with second version already cooking)
fal/AuraSR
7. NYU release Cambrian 1 - Vision Multimodal LLM that beats pretty much all other closed source competition 8-34B model size
https://huggingface.co/nyu-visionx
And.. much more like Open LLM Leaderboard got a major update, LYMSYS released Chat Vision Arena, OpenAI released a paper on CriticGPT!
What a lovely week, can’t wait for the next to see what the community is up to! Put it down in comments if I missed something 🔥
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#96 opened 7 months ago
by
ZeroWw
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alex-abb's
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7 months ago
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4820
Hi everyone!
I'm Alex, I'm 16, I've been an internship at Hugging Face for a little over a week and I've already learned a lot about using and prompting LLM models. With @victor as tutor I've just finished a space that analyzes your feelings by prompting an LLM chat model. The aim is to extend it so that it can categorize hugging face posts.
alex-abb/LLM_Feeling_Analyzer
I'm Alex, I'm 16, I've been an internship at Hugging Face for a little over a week and I've already learned a lot about using and prompting LLM models. With @victor as tutor I've just finished a space that analyzes your feelings by prompting an LLM chat model. The aim is to extend it so that it can categorize hugging face posts.
alex-abb/LLM_Feeling_Analyzer
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7 months ago
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7 months ago
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6060
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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#2 opened 7 months ago
by
D4ve-R
Great work!
1
#1 opened 7 months ago
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merve
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I love Depth Anything V2 😍
It’s Depth Anything, but scaled with both larger teacher model and a gigantic dataset!
Here's a small TLDR of paper with a lot of findings, experiments and more.
I have also created a collection that has the models, the dataset, the demo and CoreML converted model 😚 merve/depth-anything-v2-release-6671902e798cd404513ffbf5
The authors have analyzed Marigold, a diffusion based model against Depth Anything and found out what’s up with using synthetic images vs real images for MDE:
🔖 Real data has a lot of label noise, inaccurate depth maps (caused by depth sensors missing transparent objects etc) and there are many details overlooked
🔖 Synthetic data have more precise and detailed depth labels and they are truly ground-truth, but there’s a distribution shift between real and synthetic images, and they have restricted scene coverage
The authors train different image encoders only on synthetic images and find out unless the encoder is very large the model can’t generalize well (but large models generalize inherently anyway) 🧐
But they still fail encountering real images that have wide distribution in labels (e.g. diverse instances of objects) 🥲
Depth Anything v2 framework is to..
🦖 Train a teacher model based on DINOv2-G based on 595K synthetic images
🏷️ Label 62M real images using teacher model
🦕 Train a student model using the real images labelled by teacher
Result: 10x faster and more accurate than Marigold!
The authors also construct a new benchmark called DA-2K that is less noisy, highly detailed and more diverse!
It’s Depth Anything, but scaled with both larger teacher model and a gigantic dataset!
Here's a small TLDR of paper with a lot of findings, experiments and more.
I have also created a collection that has the models, the dataset, the demo and CoreML converted model 😚 merve/depth-anything-v2-release-6671902e798cd404513ffbf5
The authors have analyzed Marigold, a diffusion based model against Depth Anything and found out what’s up with using synthetic images vs real images for MDE:
🔖 Real data has a lot of label noise, inaccurate depth maps (caused by depth sensors missing transparent objects etc) and there are many details overlooked
🔖 Synthetic data have more precise and detailed depth labels and they are truly ground-truth, but there’s a distribution shift between real and synthetic images, and they have restricted scene coverage
The authors train different image encoders only on synthetic images and find out unless the encoder is very large the model can’t generalize well (but large models generalize inherently anyway) 🧐
But they still fail encountering real images that have wide distribution in labels (e.g. diverse instances of objects) 🥲
Depth Anything v2 framework is to..
🦖 Train a teacher model based on DINOv2-G based on 595K synthetic images
🏷️ Label 62M real images using teacher model
🦕 Train a student model using the real images labelled by teacher
Result: 10x faster and more accurate than Marigold!
The authors also construct a new benchmark called DA-2K that is less noisy, highly detailed and more diverse!
liked
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