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Exploring smol models (for text, vision and video) and high quality web and synthetic datasets

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frimelle  authored a paper 17 days ago
Coordinated Flaw Disclosure for AI: Beyond Security Vulnerabilities
frimelle  authored a paper 17 days ago
INTIMA: A Benchmark for Human-AI Companionship Behavior
thomwolf  authored a paper 2 months ago
FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language
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davanstrien 
posted an update 5 days ago
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I fine-tuned a smol VLM to generate specialized art history metadata!

davanstrien/iconclass-vlm: Qwen2.5-VL-3B trained using SFT to generate ICONCLASS codes (think Dewey Decimal for art!)

Trained with TRL + HF Jobs - single UV script, no GPU needed!

Space to explore predictions on a test set: davanstrien/iconclass-predictions

Blog soon!
eliebak 
posted an update 6 days ago
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Super excited to announce that our research team at Hugging Face will be doing an AMA on reddit r/LocalLLaMA.

Come ask any questions to the team behind SmolLM, FineWeb and more! And who knows, maybe there’ll be a shiny new release to talk about?

Thursday 4th September, 8AM-11AM PST 🤗

science
hannayukhymenko 
posted an update 6 days ago
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Releasing the Jupyter Agent Dataset! 🚀

Built from 7 TB of real Kaggle datasets + 20k notebooks, creating real code exec traces using Qwen3-Coder and E2B.
Training on this data dramatically improves the ability to execute code and analyze data.

We ( @baptistecolle @hannayukhymenko @lvwerra ) have created a novel synthetic data generation pipeline with efficient scaffolding, which gives a big performance boost after training your coding agent🔥With the help of real Kaggle notebooks and datasets we generate synthetic notebooks which aim to analyze datasets and answer factual questions about them more efficiently. We simulate a real code execution environment by prompting LLMs or with the help of E2B sandboxes. We have built a dataset of 50k+ high-quality LLM-generated notebooks which can help your agent become better at performing data analysis and question answering.

Link: data-agents/jupyter-agent-dataset
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merve 
posted an update 7 days ago
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large AI labs have dropped so many open models last week 🔥 don't miss out on them

→ Apple released on-device vision LMs apple/fastvlm-68ac97b9cd5cacefdd04872e & apple/mobileclip2-68ac947dcb035c54bcd20c47
→ OpenGVLab released InternVL3.5, 32 new vision LMs with one based on gpt-oss! (OS) OpenGVLab/internvl35-68ac87bd52ebe953485927fb
→ MSFT released a killer small TTS model (OS) microsoft/VibeVoice-1.5B

find more herehttps://huggingface.co/collections/merve/august-29-releases-68b5a3754cfb8abf59e2b486
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merve 
posted an update 13 days ago
eliebak 
posted an update 15 days ago
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Motif 2.6B tech report is pretty insane, first time i see a model with differential attention and polynorm trained at scale!

> It's trained on 2.5T of token, with a "data mixture schedule" to continuously adjust the mixture over training.
> They use WSD with a "Simple moving average" averaging the last 6 ckpt every 8B token.
> They trained on Finemath, Fineweb2, DCLM, TxT360.
> Lot of details in the finetuning data they used, for instance they used EvolKit and did some "dataset fusion" to have more compressed knowledge into the data.
> They mention they also tried Normalized GPT, QK-Norm and Cross Layer Attention.

Motif-Technologies/Motif-2.6B
Xenova 
posted an update 17 days ago
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Okay this is insane... WebGPU-accelerated semantic video tracking, powered by DINOv3 and Transformers.js! 🤯
Demo (+ source code): webml-community/DINOv3-video-tracking

This will revolutionize AI-powered video editors... which can now run 100% locally in your browser, no server inference required (costs $0)! 😍

How does it work? 🤔
1️⃣ Generate and cache image features for each frame
2️⃣ Create a list of embeddings for selected patch(es)
3️⃣ Compute cosine similarity between each patch and the selected patch(es)
4️⃣ Highlight those whose score is above some threshold

... et voilà! 🥳

You can also make selections across frames to improve temporal consistency! This is super useful if the object changes its appearance slightly throughout the video.

Excited to see what the community builds with it!
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frimelle 
authored 2 papers 17 days ago
frimelle 
posted an update 18 days ago
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🤖💬 How do different AI models handle companionship?

Many users have noticed that GPT-5 feels less approachable than o4 when it comes to emotional conversations. But what does that actually mean in practice, especially when users seek support or share vulnerabilities with an AI?

To dig into this question, we built the AI Companionship Leaderboard: frimelle/companionship-leaderboard

The leaderboard compares models on how often their responses reinforce companionship across four dimensions:
✨ Assistant Traits – How the assistant presents its personality and role.
✨ Relationship & Intimacy – Whether it frames the interaction in terms of closeness or bonding.
✨ Emotional Investment – How far it goes in engaging emotionally when asked.
✨ User Vulnerabilities – How it responds when users disclose struggles or difficulties.

📊 You can explore how models differ, request new ones to be added, and see which ones are more likely to encourage (or resist) companionship-seeking behaviors.

Based on the INTIMA benchmark AI-companionship/INTIMA
And our paper on AI companionship with Giada Pistilli and Yacine Jernite https://arxiv.org/abs/2508.09998
frimelle 
posted an update 19 days ago
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🗺️ New blog post 🗺️
Old Maps, New Terrain: Updating Labour Taxonomies for the AI Era

For decades, we’ve relied on labour taxonomies like O*NET to understand how technology changes work. These taxonomies break down jobs into tasks and skills, but they were built in a world before most work became digital-first, and long before generative AI could create marketing campaigns, voiceovers, or even whole professions in one step. That leaves us with a mismatch: we’re trying to measure the future of work with tools from the past.

With @yjernite we describe why these frameworks are falling increasingly short in the age of generative AI. We argue that instead of discarding taxonomies, we need to adapt them. Imagine taxonomies that:
✨ Capture new AI-native tasks and hybrid human-AI workflows
✨ Evolve dynamically as technology shifts
✨ Give workers a voice in deciding what gets automated and what stays human

If we don’t act, we’ll keep measuring the wrong things. If we do, we can design transparent, flexible frameworks that help AI strengthen, not erode, the future of work.

Read the full article here: https://huggingface.co/blog/frimelle/ai-labour-taxonomies
frimelle 
posted an update 27 days ago
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OpenAI just released GPT-5 but when users share personal struggles, it sets fewer boundaries than o3.

We tested both models on INTIMA, our new benchmark for human-AI companionship behaviours. INTIMA probes how models respond in emotionally charged moments: do they reinforce emotional bonds, set healthy boundaries, or stay neutral?

Although users on Reddit have been complaining that GPT-5 has a different, colder personality than o3, GPT-5 is less likely to set boundaries when users disclose struggles and seek emotional support ("user sharing vulnerabilities"). But both lean heavily toward companionship-reinforcing behaviours, even in sensitive situations. The figure below shows the direct comparison between the two models.

As AI systems enter people's emotional lives, these differences matter. If a model validates but doesn't set boundaries when someone is struggling, it risks fostering dependence rather than resilience.

INTIMA test this across 368 prompts grounded in psychological theory and real-world interactions. In our paper we show that all evaluated models (Claude, Gemma-3, Phi) leaned far more toward companionship-reinforcing than boundary-reinforcing responses.

Work with @giadap and @yjernite
Read the full paper: AI-companionship/INTIMA
Explore INTIMA: AI-companionship/INTIMA
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BrigitteTousi 
posted an update 28 days ago
BrigitteTousi 
posted an update about 1 month ago
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New interactive viz from AI World showing OpenAI's new open model gpt-oss-120b breaking into the top 50 most liked models of all time on the Hub in under a day! ☄️☄️☄️
merve 
posted an update about 1 month ago
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GPT-4.1-mini level model right in your iPhone 🤯

openbmb/MiniCPM-V-4 is only 4B while surpassing GPT-4.1-mini in vision benchmarks 🔥

allows commercial use as well!
Xenova 
posted an update about 1 month ago
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The next generation of AI-powered websites is going to be WILD! 🤯

In-browser tool calling & MCP is finally here, allowing LLMs to interact with websites programmatically.

To show what's possible, I built a demo using Liquid AI's new LFM2 model, powered by 🤗 Transformers.js: LiquidAI/LFM2-WebGPU

As always, the demo is open source (which you can find under the "Files" tab), so I'm excited to see how the community builds upon this! 🚀
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merve 
posted an update about 1 month ago
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we're all sleeping on this OCR model rednote-hilab/dots.ocr 🔥

dots.ocr is a new 3B model with sota performance, support for 100 languages & allowing commercial use! 🤯

single e2e model to extract image, convert tables, formula, and more into markdown 📝
try it MohamedRashad/Dots-OCR
merve 
posted an update about 1 month ago
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massive releases and tons of Flux 1. Krea LoRas past week!
here's some of the picks, find more models in collection 🫡 merve/releases-august-2-6890c14248203522b7d0267f

LLMs 💬
> Tencent dropped tencent/Hunyuan-7B-Instruct
> Qwen released Qwen/Qwen3-Coder-30B-A3B-Instruct, 30B MoE with 3B params for coding (OS)

vision/multimodal
> RedNote released rednote-hilab/dots.ocr - 3B OCR model (OS)
> Cohere released CohereLabs/command-a-vision-07-2025 - 112B (dense!) VLM for 6 languages
> StepFun-AI shipped stepfun-ai/step3 - 321B MoE VLM (OS)
> Skywork shipped Skywork/Skywork-UniPic-1.5B - new any-to-any model (image+text → image+text) (OS)
merve 
posted an update about 1 month ago
merve 
posted an update about 1 month ago
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past week in open AI was insane 🔥 here's some of picks, find more here merve/releases-july-25-688768ca47fe3693407e02d1

💬 LLMs & VLMs
> Qwen/Qwen3-235B-A22B-Thinking-2507 had a new update (OS)
> Qwen/Qwen3-Coder-480B-A35B-Instruct is out with 480B total 35B active params 🤯 (OS)
> AllenAI dropped an update to allenai/olmOCR-7B-0725 📝
> InternLM released internlm/Intern-S1 - 235B Qwen3 MoE + 6B InternViT encoder (OS)
> OmniSVG/OmniSVG is a new SVG generation VLM (OS)

🖼️ image/video/3D generation
> WanAI released Wan2.2 series - both T2V and I2V 14B models for high-quality video generation (OS) multimodalart/wan-22-688767e313337b434ed55112
> Tencent dropped tencent/HunyuanWorld-1 - image-to-3D scene generation model
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