fdaudens (Florent Daudens)

posted an update 3 days ago

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📢 SmolLM2 paper released! Learn how the 🤗 team built one of the best small language models: from data choices to training insights. Check out our findings and share your thoughts! 🤏💡

Check it out: SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model (2502.02737)

reacted to merve's post with 👍 6 days ago

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This week in open AI was 🔥 Let's recap! 🤗 merve/january-31-releases-679a10669bd4030090c5de4d
LLMs 💬
> Huge: AllenAI released new Tülu models that outperform DeepSeek R1 using Reinforcement Learning with Verifiable Reward (RLVR) based on Llama 3.1 405B 🔥
> Mistral AI is back to open-source with their "small" 24B models (base & SFT), with Apache 2.0 license 😱
> Alibaba Qwen released their 1M context length models Qwen2.5-Instruct-1M, great for agentic use with Apache 2.0 license 🔥
> Arcee AI released Virtuoso-medium, 32.8B LLMs distilled from DeepSeek V3 with dataset of 5B+ tokens
> Velvet-14B is a new family of 14B Italian LLMs trained on 10T tokens in six languages
> OpenThinker-7B is fine-tuned version of Qwen2.5-7B-Instruct on OpenThoughts dataset

VLMs & vision 👀
> Alibaba Qwen is back with Qwen2.5VL, amazing new capabilities ranging from agentic computer use to zero-shot localization 🔥
> NVIDIA released new series of Eagle2 models with 1B and 9B sizes
> DeepSeek released Janus-Pro, new any-to-any model (image-text generation from image-text input) with MIT license
> BEN2 is a new background removal model with MIT license!

Audio 🗣️
> YuE is a new open-source music generation foundation model, lyrics-to-song generation

Codebase 👩🏻‍💻
> We are open-sourcing our SmolVLM training and eval codebase! https://github.com/huggingface/smollm/tree/main/vision
> Open-R1 is open-source reproduction of R1 by @huggingface science team https://huggingface.co/blog/open-r1

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posted an update 7 days ago

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📊 R1 just built its own download dashboard!

Some fresh stats: +6M downloads for 800+ derivative models vs 2M for originals. Watch the numbers grow here: fdaudens/deepseek-download-stats

posted an update 10 days ago

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🎯 Kokoro TTS just hit v1.0! 🚀

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 ✨

Check it out: hexgrad/Kokoro-82M

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reacted to hexgrad's post with 🔥 10 days ago

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hexgrad/Kokoro-82M got an upgrade! ⬆️ More voices, more languages, pip install kokoro, and still 82M parameters.

GitHub: https://github.com/hexgrad/kokoro
PyPI: https://pypi.org/project/kokoro/
Space: hexgrad/Kokoro-TTS

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posted an update 11 days ago

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💪 The open-source community is really unstoppable:

+5M total downloads for DeepSeek models on @hf .co
+4M are from the 700 models created by the community
That's 30% more than yesterday!

posted an update 12 days ago

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🚀 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!

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reacted to Kseniase's post with 🚀 13 days ago

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7 Open-source Methods to Improve Video Generation and Understanding

AI community is making great strides toward achieving the full potential of multimodality in video generation and understanding. Last week studies showed that working with videos is now one of the main focuses for improving AI models. Another highlight of the week is that open source, once again, proves its value. For those who were impressed by DeepSeek-R1, we’re with you!

Today, we’re combining these two key focuses and bringing you a list of open-source methods for better video generation and understanding:

1. VideoLLaMA 3 model: Excels in various video and image tasks thanks to vision-centric training approach. VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding (2501.13106)

2. FILMAGENT framework assigns roles to multiple AI agents, like a director, screenwriter, actor, and cinematographer, to automate the filmmaking process in 3D virtual environments. FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in Virtual 3D Spaces (2501.12909)

3. Improving Video Generation with Human Feedback (2501.13918) proposes a new VideoReward Model and approach that uses human feedback to refine video generation models.

4. DiffuEraser video inpainting model, based on stable diffusion, is designed to fill in missing areas with detailed, realistic content and to ensure consistent structures across frames. DiffuEraser: A Diffusion Model for Video Inpainting (2501.10018)

5. MAGI is a hybrid video gen model that combines masked and casual modeling. Its key innovation, Complete Teacher Forcing (CTF), conditions masked frames on fully visible frames. Taming Teacher Forcing for Masked Autoregressive Video Generation (2501.12389)

6. Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise (2501.08331) proposes motion control, allowing users to guide how objects or the camera move in generated videos. Its noise warping algorithm replaces random noise in videos with structured noise based on motion info.

7. Video Depth Anything model estimates depth consistently in super-long videos (several minutes or more) without sacrificing quality or speed. Video Depth Anything: Consistent Depth Estimation for Super-Long Videos (2501.12375)

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reacted to AdinaY's post with 🚀 13 days ago

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🔥So many exciting releases coming from the Chinese community this month!
zh-ai-community/2025-january-6786b054f492fb223591269e

LLMs:
✨ Qwen2.5 -1M by Alibaba
Qwen/qwen25-1m-679325716327ec07860530ba
✨ InternLM3-8B-Instruct by Shanghai AI Lab
internlm/internlm3-8b-instruct
✨ MiniMax-Text-01 by MiniMax AI
MiniMaxAI/MiniMax-Text-01
✨ RWKV-7 by BlinkDL -- RNN + Transformer 👀
BlinkDL/rwkv-7-world
✨ DeepSeek-R1 by DeepSeek -- THE ONE 🙌
https://huggingface.co/deepseek-ai
✨ Baichuan-M1-14B by Baichuan - Medical 🩺
baichuan-inc/Baichuan-M1-14B-Base
✨ Qwen2.5-Math-PRM by Alibaba - Math 🔢
Qwen/Qwen2.5-Math-PRM-7B

Code:
✨ Tare by Bytedance
https://trae.ai

TTS:
✨ T2A-01-HD by MiniMax AI
https://hailuo.ai/audio
✨ LLaSA by HKUST Audio
HKUSTAudio/Llasa-3B

MLLM:
✨ Kimi k1.5 by Moonshot AI
https://kimi.ai
✨ MiniCPM-o-2_6 by OpenBMB
openbmb/MiniCPM-o-2_6
✨ Sa2VA-4B by ByteDance
ByteDance/Sa2VA-4B
✨ VideoLLaMA 3 by Alibaba DAMO
DAMO-NLP-SG/videollama3-678cdda9281a0e32fe79af15
✨ LLaVA-Mini by Chinese Academy of Sciences
ICTNLP/llava-mini-llama-3.1-8b
✨Hunyuan-7B by Tencent
tencent/Hunyuan-7B-Instruct
✨ Hunyuan 3D 2.0 by Tencent
tencent/Hunyuan3D-2
✨MiniMax-VL-01 by MiniMax AI - A non transformer based VLM 👀
MiniMaxAI/MiniMax-VL-01

Agent:
✨ UI-TARS by Bytedance
bytedance-research/UI-TARS-7B-SFT
✨ GLM-PC by Zhipu AI
https://cogagent.aminer.cn

Dataset:
✨ Fineweb-Edu-Chinese by Opencsg
opencsg/Fineweb-Edu-Chinese-V2.1
✨ Multimodal_textbook by Alibaba
DAMO-NLP-SG/multimodal_textbook
✨ MME-Finance by Hithink AI

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posted an update 13 days ago

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Yes, DeepSeek R1's release is impressive. But the real story is what happened in just 7 days after:

- Original release: 8 models, 540K downloads. Just the beginning...

- The community turned those open-weight models into +550 NEW models on Hugging Face. Total downloads? 2.5M—nearly 5X the originals.

The reason? DeepSeek models are open-weight, letting anyone build on top of them. Interesting to note that the community focused on quantized versions for better efficiency & accessibility. They want models that use less memory, run faster, and are more energy-efficient.

When you empower builders, innovation explodes. For everyone. 🚀

The most popular community model? @bartowski 's DeepSeek-R1-Distill-Qwen-32B-GGUF version — 1M downloads alone.

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posted an update 19 days ago

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What's at stake with Meta's decision to change its content moderation policy? @giadap has, by far, the most thoughtful take I’ve seen on this question. Read her op-ed: https://www.techpolicy.press/when-freedom-bites-back-meta-moderation-and-the-limits-of-tolerance/

reacted to AdinaY's post with 🔥 20 days ago

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BIG release by DeepSeek AI🔥🔥🔥

DeepSeek-R1 & DeepSeek-R1-Zero: two 660B reasoning models are here, alongside 6 distilled dense models (based on Llama & Qwen) for the community!
https://huggingface.co/deepseek-ai
deepseek-ai/DeepSeek-R1

✨ MIT License : enabling distillation for custom models
✨ 32B & 70B models match OpenAI o1-mini in multiple capabilities
✨ API live now! Access Chain of Thought reasoning with model='deepseek-reasoner'

posted an update 20 days ago

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Reminder: Don’t. Use. ChatGPT. As. A. Calculator. Seriously. 🤖

Loved listening to @sasha on Hard Fork—it really made me think.

A few takeaways that hit home:
- Individual culpability only gets you so far. The real priority: demanding accountability and transparency from companies.
- Evaluate if generative AI is the right tool for certain tasks (like search) before using it.

Curious about the full conversation? https://www.nytimes.com/2025/01/17/podcasts/hardfork-tiktok-rednote-environment.html. Give it a listen—it’s worth it! 🌍

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reacted to merve's post with ❤️ 23 days ago

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Everything that happened this week in open AI, a recap 🤠 merve/jan-17-releases-678a673a9de4a4675f215bf5

👀 Multimodal
- MiniCPM-o 2.6 is a new sota any-to-any model by OpenBMB
(vision, speech and text!)
- VideoChat-Flash-Qwen2.5-2B is new video multimodal models by OpenGVLab that come in sizes 2B & 7B in resolutions 224 & 448
- ByteDance released larger SA2VA that comes in 26B parameters
- Dataset: VRC-Bench is a new diverse benchmark for multimodal LLM reasoning performance

💬 LLMs
- MiniMax-Text-01 is a new huge language model (456B passive 45.9B active params) by MiniMaxAI with context length of 4M tokens 🤯
- Dataset: Sky-T1-data-17k is a diverse dataset used to train Sky-T1-32B
- kyutai released Helium-1-Preview-2B is a new small multilingual LM
- Wayfarer-12B is a new LLM able to write D&D 🧙🏻‍♂️
- ReaderLM-v2 is a new HTML parsing model by Jina AI

- Dria released, Dria-Agent-a-3B, new agentic coding model (Pythonic function calling) based on Qwen2.5 Coder
- Unsloth released Phi-4, faster and memory efficient Llama 3.3

🖼️ Vision
- MatchAnything is a new foundation model for matching
- FitDit is a high-fidelity VTON model based on DiT architecture

🗣️ Audio
- OuteTTS-0.3-1B is a new multilingual text-to-speech model with voice cloning and emotion control capabilities

📖 Retrieval
- lightblue released a new reranker based on Qwen2.5 LB-reranker-0.5B-v1.0 that can handle 95+ languages
- cde-small-v2 is a new sota small retrieval model by
@jxm

posted an update 25 days ago

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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.

👉 https://huggingface.co/blog/ethics-soc-7

reacted to AdinaY's post with 🔥 26 days ago

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Excited to see Alibaba DAMO Academy release a multimodel dataset for vision language pretraining on the hub🔥

Paper: 2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining (2501.00958)
Dataset: DAMO-NLP-SG/multimodal_textbook

✨ 6.5M images + 0.8B text from 22k hours of instructional videos
✨ Covers subjects like math, physics, and chemistry
✨ Apache 2.0

posted an update 26 days ago

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🔥 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.

📊 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

⚖️ 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

🎯 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

reacted to MoritzLaurer's post with ❤️ 28 days ago

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FACTS is a great paper from @GoogleDeepMind on measuring the factuality of LLM outputs. You can now download their prompt templates from @huggingface to improve LLM-based fact-checking yourself!

📏 The paper introduces the FACTS Grounding benchmark for evaluating the factuality of LLM outputs.

🤖 Fact-checking is automated by an ensemble of LLM judges that verify if a response is fully grounded in a factual reference document.

🧪 The authors tested different prompt templates on held-out data to ensure their generalization.

📚 It's highly educational to read these templates to learn how frontier labs design prompts and understand their limitations.

💾 You can now download and reuse these prompt templates via the prompt-templates library!

🔄 The library simplifies sharing prompt templates on the HF hub or locally via standardized YAML files. Let’s make LLM work more transparent and reproducible by sharing more templates like this!

Links 👇
- prompt-templates docs: https://moritzlaurer.github.io/prompt_templates/
- all templates on the HF Hub: MoritzLaurer/facts-grounding-prompts
- FACTS paper: https://storage.googleapis.com/deepmind-media/FACTS/FACTS_grounding_paper.pdf

posted an update about 2 months ago

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🔍 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

posted an update about 2 months ago

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🤝 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

Florent Daudens

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