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224
we now have more than 2000 public AI models using ModelHubMixin๐ค
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AI & ML interests
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HFforLegal's activity
Post
959
New work from Alibaba_Qwen๐ฅ
Qwen2.5-Math-PRM 7B & 72B ๐ข Process Reward Models for enhanced process supervision in the mathematical reasoning of LLMs.
Paper:
The Lessons of Developing Process Reward Models in Mathematical Reasoning (2501.07301)
Model:
Qwen/Qwen2.5-Math-PRM-7B
Qwen/Qwen2.5-Math-PRM-72B
Qwen2.5-Math-PRM 7B & 72B ๐ข Process Reward Models for enhanced process supervision in the mathematical reasoning of LLMs.
Paper:
The Lessons of Developing Process Reward Models in Mathematical Reasoning (2501.07301)
Model:
Qwen/Qwen2.5-Math-PRM-7B
Qwen/Qwen2.5-Math-PRM-72B
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1143
๐๐ปโโ๏ธ Hey there folks ,
Facebook AI just released JASCO models that make music stems .
you can try it out here : Tonic/audiocraft
hope you like it
Facebook AI just released JASCO models that make music stems .
you can try it out here : Tonic/audiocraft
hope you like it
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1653
AI agents are coming. But who's in control?
@meg , one of the best researchers in AI ethics, makes a critical point about autonomy: fully autonomous systems carry unknowable risks because they operate on computer logic rather than human logic.
The solution? Build systems that support & assist rather than override human decisions.
I highly recommend reading the blog post written by Meg, @evijit @sasha and @giadap . They define different levels of agent autonomy & provide a values-based analysis of risks, benefits, and uses of AI agents to help you make better decisions.
๐ https://huggingface.co/blog/ethics-soc-7
@meg , one of the best researchers in AI ethics, makes a critical point about autonomy: fully autonomous systems carry unknowable risks because they operate on computer logic rather than human logic.
The solution? Build systems that support & assist rather than override human decisions.
I highly recommend reading the blog post written by Meg, @evijit @sasha and @giadap . They define different levels of agent autonomy & provide a values-based analysis of risks, benefits, and uses of AI agents to help you make better decisions.
๐ https://huggingface.co/blog/ethics-soc-7
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InternLM3-8B-instruct๐ฅ Trained on just 4T tokens, it outperforms Llama3.1-8B and Qwen2.5-7B in reasoning tasks, at 75% lower cost!
internlm/internlm3-67875827c377690c01a9131d
internlm/internlm3-67875827c377690c01a9131d
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1587
๐ Let me introduce the work I've done over the past three months: ๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ฎ-๐ง๐ฎ๐ถ๐๐ฎ๐ป-๐ฏ๐ and ๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ฎ-๐ง๐ฎ๐ถ๐๐ฎ๐ป-๐ฏ๐-๐๐ป๐๐๐ฟ๐๐ฐ๐, now open-sourced on ๐ค Hugging Face.
๐น๐ถ๐ฎ๐ป๐ด๐ต๐๐๐ป/๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ฎ-๐ง๐ฎ๐ถ๐๐ฎ๐ป-๐ฏ๐: This model is built on top of ๐บ๐ฒ๐๐ฎ-๐น๐น๐ฎ๐บ๐ฎ/๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ฎ-๐ฏ๐ with continual pretraining. The training dataset consists of a mixture of Traditional Chinese and multilingual texts in specific proportions, including 20B tokens of Traditional Chinese text.
๐น๐ถ๐ฎ๐ป๐ด๐ต๐๐๐ป/๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ฎ-๐ง๐ฎ๐ถ๐๐ฎ๐ป-๐ฏ๐-๐๐ป๐๐๐ฟ๐๐ฐ๐: This is a fine-tuned conversational model based on the foundation model.
This Llama-3.2-Taiwan open-source project is currently a one-person effort (yes, I did everything from text preparation โ so exhausting!). If you're interested, feel free to join the Discord server for discussions.
๐ ฑ๐ ด๐ ฝ๐ ฒ๐ ท๐ ผ๐ ฐ๐๐ บ๐ ธ๐ ฝ๐ ถ
The evaluation was conducted using ikala/tmmluplus, though the README page does not yet reflect the latest results. The performance is close to the previous versions, indicating that further improvements might require adding more specialized knowledge in the datasets.
๐ ฐ ๐ ฒ๐ ฐ๐ ป๐ ป ๐ ต๐ พ๐ ๐๐๐ ฟ๐ ฟ๐ พ๐๐
If anyone is willing to provide compute resources, it would be greatly appreciated to help this project continue and grow. ๐ช
---
๐๏ธ Foundation model: lianghsun/Llama-3.2-Taiwan-3B
๐ค Instruction model: lianghsun/Llama-3.2-Taiwan-3B-Instruct
โก GGUF: lianghsun/Llama-3.2-Taiwan-3B-Instruct-GGUF
๐น๐ถ๐ฎ๐ป๐ด๐ต๐๐๐ป/๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ฎ-๐ง๐ฎ๐ถ๐๐ฎ๐ป-๐ฏ๐: This model is built on top of ๐บ๐ฒ๐๐ฎ-๐น๐น๐ฎ๐บ๐ฎ/๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ฎ-๐ฏ๐ with continual pretraining. The training dataset consists of a mixture of Traditional Chinese and multilingual texts in specific proportions, including 20B tokens of Traditional Chinese text.
๐น๐ถ๐ฎ๐ป๐ด๐ต๐๐๐ป/๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ฎ-๐ง๐ฎ๐ถ๐๐ฎ๐ป-๐ฏ๐-๐๐ป๐๐๐ฟ๐๐ฐ๐: This is a fine-tuned conversational model based on the foundation model.
This Llama-3.2-Taiwan open-source project is currently a one-person effort (yes, I did everything from text preparation โ so exhausting!). If you're interested, feel free to join the Discord server for discussions.
๐ ฑ๐ ด๐ ฝ๐ ฒ๐ ท๐ ผ๐ ฐ๐๐ บ๐ ธ๐ ฝ๐ ถ
The evaluation was conducted using ikala/tmmluplus, though the README page does not yet reflect the latest results. The performance is close to the previous versions, indicating that further improvements might require adding more specialized knowledge in the datasets.
๐ ฐ ๐ ฒ๐ ฐ๐ ป๐ ป ๐ ต๐ พ๐ ๐๐๐ ฟ๐ ฟ๐ พ๐๐
If anyone is willing to provide compute resources, it would be greatly appreciated to help this project continue and grow. ๐ช
---
๐๏ธ Foundation model: lianghsun/Llama-3.2-Taiwan-3B
๐ค Instruction model: lianghsun/Llama-3.2-Taiwan-3B-Instruct
โก GGUF: lianghsun/Llama-3.2-Taiwan-3B-Instruct-GGUF
- 4 replies
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2888
MiniMax, the company behind Hailuo_AI, has joined the open source community by releasing both models and demos of MiniMax-Text-01 & MiniMax-VL-01๐ฅ
- Model
MiniMaxAI/MiniMax-VL-01
MiniMaxAI/MiniMax-Text-01
- Demo
MiniMaxAI/MiniMax-VL-01
MiniMaxAI/MiniMax-Text-01
โจ MiniMax-text-01:
- 456B with 45.9B activated per token
- Combines Lightning Attention, Softmax Attention, and MoE for optimal performance
- Training context up to 1M tokens, inference handles 4M tokens
โจ MiniMax-VL-01:
- ViT-MLP-LLM framework ( non-transformer๐)
- Handles image inputs from 336ร336 to 2016ร2016
- 694M image-caption pairs + 512B tokens processed across 4 stages
- Model
MiniMaxAI/MiniMax-VL-01
MiniMaxAI/MiniMax-Text-01
- Demo
MiniMaxAI/MiniMax-VL-01
MiniMaxAI/MiniMax-Text-01
โจ MiniMax-text-01:
- 456B with 45.9B activated per token
- Combines Lightning Attention, Softmax Attention, and MoE for optimal performance
- Training context up to 1M tokens, inference handles 4M tokens
โจ MiniMax-VL-01:
- ViT-MLP-LLM framework ( non-transformer๐)
- Handles image inputs from 336ร336 to 2016ร2016
- 694M image-caption pairs + 512B tokens processed across 4 stages
-
- 1 reply
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3094
MiniCPM-o2.6 ๐ฅ an end-side multimodal LLMs released by OpenBMB from the Chinese community
Model: openbmb/MiniCPM-o-2_6
โจ Real-time English/Chinese conversation, emotion control and ASR/STT
โจ Real-time video/audio understanding
โจ Processes up to 1.8M pixels, leads OCRBench & supports 30+ languages
Model: openbmb/MiniCPM-o-2_6
โจ Real-time English/Chinese conversation, emotion control and ASR/STT
โจ Real-time video/audio understanding
โจ Processes up to 1.8M pixels, leads OCRBench & supports 30+ languages
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2205
๐๐ปโโ๏ธHey there folks , Open LLM Europe just released Lucie 7B-Instruct model , a billingual instruct model trained on open data ! You can check out my unofficial demo here while we wait for the official inference api from the group :
Tonic/Lucie-7B hope you like it ๐
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2235
๐ฅ The AI Agent hype is real! This blog post deep dives into everything you need to know before deploying them: from key definitions to practical recommendations. A must-read for anyone building the future of autonomous systems.
๐ Key insight: A clear table breaking down the 5 levels of AI agents - from simple processors to fully autonomous systems. Essential framework for understanding where your agent stands on the autonomy spectrum
โ๏ธ Deep analysis of 15 core values reveals critical trade-offs: accuracy, privacy, safety, equity & more. The same features that make agents powerful can make them risky. Understanding these trade-offs is crucial for responsible deployment
๐ฏ 6 key recommendations for the road ahead:
- Create rigorous evaluation protocols
- Study societal effects
- Understand ripple effects
- Improve transparency
- Open source can make a positive difference
- Monitor base model evolution
Read the blog post: https://huggingface.co/blog/ethics-soc-7 Brillant work by @meg @evijit @sasha @giadap
๐ Key insight: A clear table breaking down the 5 levels of AI agents - from simple processors to fully autonomous systems. Essential framework for understanding where your agent stands on the autonomy spectrum
โ๏ธ Deep analysis of 15 core values reveals critical trade-offs: accuracy, privacy, safety, equity & more. The same features that make agents powerful can make them risky. Understanding these trade-offs is crucial for responsible deployment
๐ฏ 6 key recommendations for the road ahead:
- Create rigorous evaluation protocols
- Study societal effects
- Understand ripple effects
- Improve transparency
- Open source can make a positive difference
- Monitor base model evolution
Read the blog post: https://huggingface.co/blog/ethics-soc-7 Brillant work by @meg @evijit @sasha @giadap
Post
3630
Published a new blogpost ๐
In this blogpost I have gone through the transformers' architecture emphasizing how shapes propagate throughout each layer.
๐ https://huggingface.co/blog/not-lain/tensor-dims
some interesting takeaways :
In this blogpost I have gone through the transformers' architecture emphasizing how shapes propagate throughout each layer.
๐ https://huggingface.co/blog/not-lain/tensor-dims
some interesting takeaways :
Post
587
LLaVA-Mini๐ฅ A efficient multimodal model for image and video understanding released by Chinese Academy of Sciences
Model: ICTNLP/llava-mini-llama-3.1-8b
Paper: LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token (2501.03895)
โจ Matches LLaVA-v1.5 using just 1 vision token
โจ Delivers <40ms response time
โจ Reduces vision tokens while maintaining strong visual understanding
Model: ICTNLP/llava-mini-llama-3.1-8b
Paper: LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token (2501.03895)
โจ Matches LLaVA-v1.5 using just 1 vision token
โจ Delivers <40ms response time
โจ Reduces vision tokens while maintaining strong visual understanding
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Excited to see Alibaba DAMO Academy release a multimodel dataset for vision language pretraining on the hub๐ฅ
Paper: 2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining (2501.00958)
Dataset: DAMO-NLP-SG/multimodal_textbook
โจ 6.5M images + 0.8B text from 22k hours of instructional videos
โจ Covers subjects like math, physics, and chemistry
โจ Apache 2.0
Paper: 2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining (2501.00958)
Dataset: DAMO-NLP-SG/multimodal_textbook
โจ 6.5M images + 0.8B text from 22k hours of instructional videos
โจ Covers subjects like math, physics, and chemistry
โจ Apache 2.0
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Cool to see
@ylecun
joining the top 10 of most followed on HF!
(and leaderboard by @mvaloatto is here: mvaloatto/TCTF)
(and leaderboard by @mvaloatto is here: mvaloatto/TCTF)
- 2 replies
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715
Hey everyone ๐ค!
Check out this new Virtual Try Off model (based on SD1.5): 1aurent/TryOffAnyone
This model isn't as accurate as others (e.g. xiaozaa/cat-try-off-flux based on FLUX.1) but it sure is fast!
Check out this new Virtual Try Off model (based on SD1.5): 1aurent/TryOffAnyone
This model isn't as accurate as others (e.g. xiaozaa/cat-try-off-flux based on FLUX.1) but it sure is fast!
Post
3593
The Chinese community is shipping ๐ข
DeepSeek V3 (685 B MoE) has quietly released on the hub!
Base: deepseek-ai/DeepSeek-V3-Base
Instruct: deepseek-ai/DeepSeek-V3
Canโt wait to see whatโs next!
DeepSeek V3 (685 B MoE) has quietly released on the hub!
Base: deepseek-ai/DeepSeek-V3-Base
Instruct: deepseek-ai/DeepSeek-V3
Canโt wait to see whatโs next!
-
- 1 reply
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QvQ-72B-Preview๐ an open weight model for visual reasoning just released by Alibaba_Qwen team
Qwen/qvq-676448c820912236342b9888
โจ Combines visual understanding & language reasoning.
โจ Scores 70.3 on MMMU
โจ Outperforms Qwen2-VL-72B-Instruct in complex problem-solving
Qwen/qvq-676448c820912236342b9888
โจ Combines visual understanding & language reasoning.
โจ Scores 70.3 on MMMU
โจ Outperforms Qwen2-VL-72B-Instruct in complex problem-solving
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๐ From instruction-following to creative storytelling, dive into 2024's most impactful AI datasets! These gems are shaping everything from scientific research to video understanding.
Check it out: huggingface/open-source-ai-year-in-review-2024
Check it out: huggingface/open-source-ai-year-in-review-2024
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๐ค Want to share your AI models while protecting your work? Licenses are key!
Fascinating to see that nearly 60% of models on the Hub use Apache & MIT licenses.
Explore the viz here: huggingface/open-source-ai-year-in-review-2024
Fascinating to see that nearly 60% of models on the Hub use Apache & MIT licenses.
Explore the viz here: huggingface/open-source-ai-year-in-review-2024