HuggingFaceM4 (HuggingFaceM4)

lewtun

posted an update about 14 hours ago

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We are reproducing the full DeepSeek R1 data and training pipeline so everybody can use their recipe. Instead of doing it in secret we can do it together in the open!

🧪 Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1.

🧠 Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code.

🔥 Step 3: show we can go from base model -> SFT -> RL via multi-stage training.

Follow along: https://github.com/huggingface/open-r1

merve

posted an update 1 day ago

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Oof, what a week! 🥵 So many things have happened, let's recap! merve/jan-24-releases-6793d610774073328eac67a9

Multimodal 💬
- We have released SmolVLM -- tiniest VLMs that come in 256M and 500M, with it's retrieval models ColSmol for multimodal RAG 💗
- UI-TARS are new models by ByteDance to unlock agentic GUI control 🤯 in 2B, 7B and 72B
- Alibaba DAMO lab released VideoLlama3, new video LMs that come in 2B and 7B
- MiniMaxAI released Minimax-VL-01, where decoder is based on MiniMax-Text-01 456B MoE model with long context
- Dataset: Yale released a new benchmark called MMVU
- Dataset: CAIS released Humanity's Last Exam (HLE) a new challenging MM benchmark

LLMs 📖
- DeepSeek-R1 & DeepSeek-R1-Zero: gigantic 660B reasoning models by DeepSeek, and six distilled dense models, on par with o1 with MIT license! 🤯
- Qwen2.5-Math-PRM: new math models by Qwen in 7B and 72B
- NVIDIA released AceMath and AceInstruct, new family of models and their datasets (SFT and reward ones too!)

Audio 🗣️
- Llasa is a new speech synthesis model based on Llama that comes in 1B,3B, and 8B
- TangoFlux is a new audio generation model trained from scratch and aligned with CRPO

Image/Video/3D Generation ⏯️
- Flex.1-alpha is a new 8B pre-trained diffusion model by ostris similar to Flux
- tencent released Hunyuan3D-2, new 3D asset generation from images

6 replies

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clem

posted an update 1 day ago

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The 🐳 just crossed 10,000 followers on HF

https://huggingface.co./deepseek-ai

dylanebert

posted an update 1 day ago

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⚙️ Convert .ply to .splat

i've created a simple space to convert .ply gaussian splat files to .splat format

dylanebert/ply-to-splat

merve

posted an update 1 day ago

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smolagents can see 🔥
we just shipped vision support to smolagents 🤗 agentic computers FTW

you can now:
💻 let the agent get images dynamically (e.g. agentic web browser)
📑 pass images at the init of the agent (e.g. chatting with documents, filling forms automatically etc)
with few LoC change! 🤯
you can use transformers models locally (like Qwen2VL) OR plug-in your favorite multimodal inference provider (gpt-4o, antrophic & co) 🤠

read our blog http://hf.co/blog/smolagents-can-see

m-ric

posted an update 1 day ago

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Today we make the biggest release in smolagents so far: 𝘄𝗲 𝗲𝗻𝗮𝗯𝗹𝗲 𝘃𝗶𝘀𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀, 𝘄𝗵𝗶𝗰𝗵 𝗮𝗹𝗹𝗼𝘄𝘀 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝘄𝗲𝗯 𝗯𝗿𝗼𝘄𝘀𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀! 🥳

Our agents can now casually open up a web browser, and navigate on it by scrolling, clicking elements on the webpage, going back, just like a user would.

The demo below shows Claude-3.5-Sonnet browsing GitHub for task: "Find how many commits the author of the current top trending repo did over last year."
Hi @mlabonne !

Go try it out, it's the most cracked agentic stuff I've seen in a while 🤯 (well, along with OpenAI's Operator who beat us by one day)

For more detail, read our announcement blog 👉 https://huggingface.co./blog/smolagents-can-see
The code for the web browser example is here 👉 https://github.com/huggingface/smolagents/blob/main/examples/vlm_web_browser.py

3 replies

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andito

posted an update 3 days ago

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𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱'𝘀 𝘀𝗺𝗮𝗹𝗹𝗲𝘀𝘁 𝘃𝗶𝘀𝗶𝗼𝗻 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹!

We’re thrilled to share 𝗦𝗺𝗼𝗹𝗩𝗟𝗠 (256M & 500M)—the smallest Visual Language Models ever built. Think: running on <1GB of GPU memory—you can fine-tune it on your laptop and run it on your toaster!

Why It’s Game-Changing:
- 𝗢𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝘀 𝗟𝗮𝗿𝗴𝗲𝗿 𝗠𝗼𝗱𝗲𝗹𝘀: Even the 256M model surpasses our SOTA 80B-parameter model from just 17 months ago. Over 300x reduction!
𝗠𝗶𝗴𝗵𝘁𝘆 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: The 256M version delivers 80% of our 2.2B model’s performance, and the 500M version hits 90%
𝗟𝗶𝗴𝗵𝘁𝗻𝗶𝗻𝗴-𝗙𝗮𝘀𝘁 𝗦𝗲𝗮𝗿𝗰𝗵: SmolVLM integrates with ColiPali for state-of-the-art retrieval speeds—on par with models 10x bigger. That means cheaper, faster indexing and real-world impact.

What’s New Under the Hood:
- 𝗡𝗲𝘄 𝗩𝗶𝘀𝗶𝗼𝗻 𝗘𝗻𝗰𝗼𝗱𝗲𝗿: Smaller overall size (400M -> 93M), but with higher resolution.
- 𝗛𝗶𝗴𝗵𝗲𝗿 𝗣𝗶𝘅𝗲𝗹𝘀/𝗧𝗼𝗸𝗲𝗻: 4096 vs. 1820—more efficient image processing.
- 𝗦𝗺𝗮𝗿𝘁 𝗧𝗼𝗸𝗲𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Faster training and a performance boost.

Check our blog: https://huggingface.co./blog/smolervlm
The models: HuggingFaceTB/smolvlm-256m-and-500m-6791fafc5bb0ab8acc960fb0
The demo: HuggingFaceTB/SmolVLM-256M-Demo

1 reply

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merve

posted an update 8 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

merve

posted an update 9 days ago

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New smolagents example landed on Hugging Face cookbook 🤠

Learn how to create an inventory managing multi-agent system with smolagents, MongoDB and DeepSeek Chat 📖 https://huggingface.co./learn/cookbook/mongodb_smolagents_multi_micro_agents

m-ric

posted an update 10 days ago

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𝗠𝗶𝗻𝗶𝗠𝗮𝘅'𝘀 𝗻𝗲𝘄 𝗠𝗼𝗘 𝗟𝗟𝗠 𝗿𝗲𝗮𝗰𝗵𝗲𝘀 𝗖𝗹𝗮𝘂𝗱𝗲-𝗦𝗼𝗻𝗻𝗲𝘁 𝗹𝗲𝘃𝗲𝗹 𝘄𝗶𝘁𝗵 𝟰𝗠 𝘁𝗼𝗸𝗲𝗻𝘀 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗹𝗲𝗻𝗴𝘁𝗵 💥

This work from Chinese startup @MiniMax-AI introduces a novel architecture that achieves state-of-the-art performance while handling context windows up to 4 million tokens - roughly 20x longer than current models. The key was combining lightning attention, mixture of experts (MoE), and a careful hybrid approach.

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

🏗️ MoE with novel hybrid attention:
‣ Mixture of Experts with 456B total parameters (45.9B activated per token)
‣ Combines Lightning attention (linear complexity) for most layers and traditional softmax attention every 8 layers

🏆 Outperforms leading models across benchmarks while offering vastly longer context:
‣ Competitive with GPT-4/Claude-3.5-Sonnet on most tasks
‣ Can efficiently handle 4M token contexts (vs 256K for most other LLMs)

🔬 Technical innovations enable efficient scaling:
‣ Novel expert parallel and tensor parallel strategies cut communication overhead in half
‣ Improved linear attention sequence parallelism, multi-level padding and other optimizations achieve 75% GPU utilization (that's really high, generally utilization is around 50%)

🎯 Thorough training strategy:
‣ Careful data curation and quality control by using a smaller preliminary version of their LLM as a judge!

Overall, not only is the model impressive, but the technical paper is also really interesting! 📝
It has lots of insights including a great comparison showing how a 2B MoE (24B total) far outperforms a 7B model for the same amount of FLOPs.

Read it in full here 👉 MiniMax-01: Scaling Foundation Models with Lightning Attention (2501.08313)
Model here, allows commercial use <100M monthly users 👉 MiniMaxAI/MiniMax-Text-01

lvwerra

authored a paper 10 days ago

Towards Best Practices for Open Datasets for LLM Training

Paper • 2501.08365 • Published 11 days ago • 47

thomwolf

authored a paper 10 days ago

Towards Best Practices for Open Datasets for LLM Training

Paper • 2501.08365 • Published 11 days ago • 47

m-ric

posted an update 10 days ago

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𝗪𝗲'𝘃𝗲 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝘀𝗺𝗼𝗹𝗮𝗴𝗲𝗻𝘁𝘀 𝘃𝟭.𝟯.𝟬 🚀, and it comes with a major feature: you can now log agent runs using OpenTelemetry to inspect them afterwards! 📊

This interactive format is IMO much easier to inspect big multi-step runs than endless console logs.

The setup is very easy, in a few lines of code.

Find a tutorial here 👉 https://huggingface.co./docs/smolagents/tutorials/inspect_runs

4 replies

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MoritzLaurer

posted an update 11 days ago

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Microsoft's rStar-Math paper claims that 🤏 ~7B models can match the math skills of o1 using clever train- and test-time techniques. You can now download their prompt templates from Hugging Face !

📏 The paper introduces rStar-Math, which claims to rival OpenAI o1's math reasoning capabilities by integrating Monte Carlo Tree Search (MCTS) with step-by-step verified reasoning trajectories.
🤖 A Process Preference Model (PPM) enables fine-grained evaluation of intermediate steps, improving training data quality.
🧪 The system underwent four rounds of self-evolution, progressively refining both the policy and reward models to tackle Olympiad-level math problems—without GPT-4-based data distillation.
💾 While we wait for the release of code and datasets, you can already download the prompts they used from the HF Hub!

Details and links here 👇
Prompt-templates docs: https://moritzlaurer.github.io/prompt_templates/
Templates on the hub: MoritzLaurer/rstar-math-prompts
Prompt-templates collection: MoritzLaurer/prompt-templates-6776aa0b0b8a923957920bb4
Paper: https://arxiv.org/pdf/2501.04519

meg

posted an update 12 days ago

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💫...And we're live!💫 Seasonal newsletter from ethicsy folks at Hugging Face, exploring the ethics of "AI Agents"
https://huggingface.co./blog/ethics-soc-7
Our analyses found:
- There's a spectrum of "agent"-ness
- *Safety* is a key issue, leading to many other value-based concerns
Read for details & what to do next!
With @evijit , @giadap , and @sasha

yjernite

posted an update 12 days ago

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🤗👤 💻 Speaking of AI agents ...
...Is easier with the right words ;)

My colleagues @meg @evijit @sasha and @giadap just published a wonderful blog post outlining some of the main relevant notions with their signature blend of value-informed and risk-benefits contrasting approach. Go have a read!

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

davanstrien

posted an update 12 days ago

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Introducing scandi-fine-web-cleaner davanstrien/scandi-fine-web-cleaner, the first model trained on FineWeb-C community annotations!

FineWeb2 is a massive multilingual dataset for pre-training language models. Like any web-scale dataset, it contains low-quality content. How can we improve it?

Over the past months, an amazing community of 400+ annotators has been labelling content quality (using Argilla) across 23 languages through the FineWeb-C initiative.

Today, I'm happy to share the first classifier trained on this data.

🔍 What we've built:

- A lightweight classifier that efficiently removes low-quality content
- 90%+ precision demonstrated on Danish & Swedish
- Can process the 43M+ documents in Danish FineWeb2 with minimal compute

🌍 Why this matters: The approach can be reproduced for any of the 23 languages in FineWeb-C ( data-is-better-together/fineweb-c). We can improve training data quality at scale without massive compute resources by starting with community annotations and training small, efficient classifiers.

Want to build a classifier for your language? Check out the full blog post with code examples and implementation details: https://danielvanstrien.xyz/posts/2025/FineWeb-c/scandinavian-content-filtering-fineweb.html

1 reply

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merve

posted an update 13 days ago

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there's a new multimodal retrieval model in town 🤠
LlamaIndex released vdr-2b-multi-v1
> uses 70% less image tokens, yet outperforming other dse-qwen2 based models
> 3x faster inference with less VRAM 💨
> shrinkable with matryoshka 🪆
> can do cross-lingual retrieval!
Collection: llamaindex/visual-document-retrieval-678151d19d2758f78ce910e1 (with models and datasets)
Demo: llamaindex/multimodal_vdr_demo
Learn more from their blog post here https://huggingface.co./blog/vdr-2b-multilingual 📖

m-ric

posted an update 14 days ago

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𝗢𝗦-𝗚𝗲𝗻𝗲𝘀𝗶𝘀: 𝗻𝗲𝘄 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝗮𝗽𝗲𝗿 𝗽𝗿𝗼𝗽𝗼𝘀𝗲𝘀 𝗮 𝗻𝗼𝘃𝗲𝗹 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗺𝗲𝘁𝗵𝗼𝗱 𝗳𝗼𝗿 𝗖𝗹𝗮𝘂𝗱𝗲-𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿-𝗨𝘀𝗲-𝗹𝗶𝗸𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, 𝘄𝗶𝘁𝗵 𝗶𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀! 🔥

The main bottleneck in building GUI agents it to find training data.
GUI Agent trajectories are not easy to get by. Crowdsourcing trajectories, then manually annotating them, could be an option, but at scale, it's hard to do

You could use synthetic data generation (ask 1000s small existing GUI agents to solve tasks, keep only successful runs). But then it's hard to come up with many high level-tasks.

➡️ Well, a novel technique was just published that creates a new promising paradigm for synthetic data generation: Shanghai AI Lab researchers propose OS-Genesis, a novel way to create training data for GUI agents that flips the traditional approach on its head. Instead of starting with predefined tasks and having humans or machines execute them, OS-Genesis first explores the interface naturally, then derives meaningful tasks from those interactions.

🔍 Exploration-driven vs task-driven approach:
‣ Instead of starting with tasks, OS-Genesis first explores GUIs by clicking and interacting
‣ It then reverse-engineers high-level tasks from successful interaction patterns
‣ This leads to more natural and diverse training data than predefined tasks

🎯 Novel reward model for trajectory quality:
‣ Rather than discarding incomplete trajectories, OS-Genesis scores them based on coherence and completion
‣ This preserves valuable partial successes that would otherwise be wasted

🏆 Superior results across environments:
‣ Nearly doubles performance on AndroidWorld (9.8% → 17.4%)

By the way, this field of GUI agents is still in infancy, so you can still make a difference with "low-cost" setups: their paper gets SOTA results with only 8xA100!

Read the paper here 👉 OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2412.19723)

MoritzLaurer

posted an update 15 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

HuggingFaceM4

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Towards Best Practices for Open Datasets for LLM Training

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