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lewtun  authored a paper 7 days ago
SmolVLM: Redefining small and efficient multimodal models
cyrilzakka  authored a paper 7 days ago
SmolVLM: Redefining small and efficient multimodal models
Xenova  authored a paper 7 days ago
SmolVLM: Redefining small and efficient multimodal models
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tomaarsen 
posted an update about 4 hours ago
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I just released Sentence Transformers v4.1; featuring ONNX and OpenVINO backends for rerankers offering 2-3x speedups and improved hard negatives mining which helps prepare stronger training datasets. Details:

🏎️ ONNX, OpenVINO, Optimization, Quantization
- I've added ONNX and OpenVINO support with just one extra argument: "backend" when loading the CrossEncoder reranker, e.g.: CrossEncoder("cross-encoder/ms-marco-MiniLM-L6-v2", backend="onnx")
- The export_optimized_onnx_model, export_dynamic_quantized_onnx_model, and export_static_quantized_openvino_model functions now work with CrossEncoder rerankers, allowing you to optimize (e.g. fusions, gelu approximations, etc.) or quantize (int8 weights) rerankers.
- I've uploaded ~340 ONNX & OpenVINO models for all existing models under the cross-encoder Hugging Face organization. You can use these without having to export when loading.

⛏ Improved Hard Negatives Mining
- Added 'absolute_margin' and 'relative_margin' arguments to mine_hard_negatives.
- absolute_margin ensures that sim(query, negative) < sim(query, positive) - absolute_margin, i.e. an absolute margin between the negative & positive similarities.
- relative_margin ensures that sim(query, negative) < sim(query, positive) * (1 - relative_margin), i.e. a relative margin between the negative & positive similarities.
- Inspired by the excellent NV-Retriever paper from NVIDIA.

And several other small improvements. Check out the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v4.1.0

With this release, I introduce near-feature parity between the SentenceTransformer embedding & CrossEncoder reranker models, which I've wanted to do for quite some time! With rerankers very strongly supported now, it's time to look forward to other useful architectures!
fdaudens 
posted an update 4 days ago
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Want AI that truly understands your country's culture? Public institutions are sitting on the next AI revolution - and here's the practical guide to unlock it.

I've had fascinating conversations recently about sovereign AI, with people trying to solve this recurring question: "How do we build AI that truly understands our culture?"

This guide by @evijit and @yjernite brings lots of insights about this question. It's not just about throwing data at models. It's about partnering cultural expertise with tech infrastructure in ways we're just starting to figure out.

An example? The National Library of Norway already has 150+ AI models on Hugging Face. They're not just digitizing books - they're building AI that thinks in Norwegian, understands Norwegian values, and serves Norwegian citizens.

This is sovereign AI in practice: technology that understands your culture, values, and languages.

Especially loved the practical examples on how to do this:
- Real examples from museums, libraries, and government agencies
- How to convert complex documents (PDFs, PowerPoints) into ML-ready formats
- Code templates for processing public data
- Technical recipes for sharing datasets on open platforms

The stakes? Citizens' ability to leverage their collective digital intelligence.

The technology is ready. The infrastructure exists. The guide shows exactly how to use it. What's needed is your cultural expertise to shape these tools.

Check it out: https://huggingface.co/blog/evijit/public-org-data-ai

P.s.: Building cool projects in a public institution? Share them in the comments for others to learn from!
fdaudens 
posted an update 5 days ago
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Do chatbots lie about Céline Dion? We now have answers, not speculation.

Ai2 just released OLMoTrace and it's a game-changer for transparency. You can literally see where an AI's responses come from in its training data - in real time.

The demo shows results about Céline. So I tried it out myself! Watch what happens in the video.

For journalists, researchers studying hallucinations and anyone who needs to trust their AI, this is like getting X-ray vision into AI systems. When the model made claims, I could instantly verify them against original sources. When it hallucinated, I could see why.

You can finally 1) understand how LLMs actually work and 2) verify if what they're saying is true. No more blind trust.

This pushes the open data movement to the next level.

👉 Blog post: https://allenai.org/blog/olmotrace
👉 Paper: https://www.datocms-assets.com/64837/1743890415-olmotrace.pdf

P.S.: A word of caution: never use a chatbot as a knowledge base. It's not Google. Better use it with a connection to the internet.
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fdaudens 
posted an update 6 days ago
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🎨 Designers, meet OmniSVG! This new model helps you create professional vector graphics from text/images, generate editable SVGs from icons to detailed characters, convert rasters to vectors, maintain style consistency with references, and integrate into your workflow.

@OmniSVG
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davanstrien 
posted an update 6 days ago
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1501
I've created a v1 dataset ( davanstrien/reasoning-required) and model ( davanstrien/ModernBERT-based-Reasoning-Required) to help curate "wild text" data for generating reasoning examples beyond the usual code/math/science domains.

- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions

My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.

This significantly reduces computation costs while expanding reasoning dataset domain coverage.
lewtun 
authored a paper 7 days ago
cyrilzakka 
authored a paper 7 days ago
Xenova 
authored a paper 7 days ago
lvwerra 
authored a paper 7 days ago
reach-vb 
authored a paper 7 days ago
mfarre 
authored a paper 7 days ago
loubnabnl 
authored a paper 7 days ago
andito 
authored a paper 7 days ago
fdaudens 
posted an update 8 days ago
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I read the 456-page AI Index report so you don't have to (kidding). The wild part? While AI gets ridiculously more accessible, the power gap is actually widening:

1️⃣ The democratization of AI capabilities is accelerating rapidly:
- The gap between open and closed models is basically closed: difference in benchmarks like MMLU and HumanEval shrunk to just 1.7% in 2024
- The cost to run GPT-3.5-level performance dropped 280x in 2 years
- Model size is shrinking while maintaining performance - Phi-3-mini hitting 60%+ MMLU at fraction of parameters of early models like PaLM

2️⃣ But we're seeing concerning divides deepening:
- Geographic: US private investment ($109B) dwarfs everyone else - 12x China's $9.3B
- Research concentration: US and China dominate highly-cited papers (50 and 34 respectively in 2023), while next closest is only 7
- Gender: Major gaps in AI skill penetration rates - US shows 2.39 vs 1.71 male/female ratio

The tech is getting more accessible but the benefits aren't being distributed evenly. Worth thinking about as these tools become more central to the economy.

Give it a read - fascinating portrait of where AI is heading! https://hai-production.s3.amazonaws.com/files/hai_ai_index_report_2025.pdf
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BrigitteTousi 
posted an update 8 days ago
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AI agents are transforming how we interact with technology, but how sustainable are they? 🌍

Design choices — like model size and structure — can massively impact energy use and cost. ⚡💰 The key takeaway: smaller, task-specific models can be far more efficient than large, general-purpose ones.

🔑 Open-source models offer greater transparency, allowing us to track energy consumption and make more informed decisions on deployment. 🌱 Open-source = more efficient, eco-friendly, and accountable AI.

Read our latest, led by @sasha with assists from myself + @yjernite 🤗
https://huggingface.co/blog/sasha/ai-agent-sustainability
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clem 
posted an update 10 days ago
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Llama 4 is in transformers!

Fun example using the instruction-tuned Maverick model responding about two images, using tensor parallel for maximum speed.

From https://huggingface.co/blog/llama4-release
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fdaudens 
posted an update 10 days ago
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See that purple banner on the Llama 4 models? It's Xet storage, and this is actually huge for anyone building with AI models. Let's geek out a little bit 🤓

Current problem: AI models are massive files using Git LFS. But with models getting bigger and downloads exploding, we needed something better.
Xet lets you version large files like code, with compression and deduplication, all Git-compatible. That means less bandwidth, faster sharing, and smoother collaboration.

Real numbers: ~25% deduplication on Llama 4 models, hitting ~40% for finetunes.

Scale matters here - the Hub served 2B model downloads in 30 days, Llama models alone at 60M. The upcoming Llama 4 Behemoth has 2T parameters! Xet's chunk-based system was built exactly for this.

This is the kind of engineering that makes the next wave of large models actually usable. Kudos to the team! 🧨

Check out the models collection: meta-llama/llama-4-67f0c30d9fe03840bc9d0164
jeffboudier 
posted an update 10 days ago
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Llama4 is out and Scout is already on the Dell Enterprise Hub to deploy on Dell systems 👉 dell.huggingface.co
fdaudens 
posted an update 11 days ago
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"Am I going to be replaced by AI?" - Crucial question, but maybe we're asking the wrong one.

📈 There's a statistic from my reads this week that stays with me: Tomer Cohen, LinkedIn's CPO, shares to Jeremy Kahn that 70% of skills used in most jobs will change by 2030. Not jobs disappearing, but transforming. And he calls out bad leadership: "If in one year's time, you are disappointed that your workforce is not 'AI native,' it is your fault."

🔄 Apparently, the Great Recalibration has begun. We're now heading into an era where AI is fundamentally redefining the nature of work itself, by forcing a complete reassessment of human value in the workplace, according to a piece in Fast Company. But it might be driven more by "the need for humans to change the way they work" than AI.

⚡ The Washington Post draws a crucial parallel: We're facing an "AI shock" similar to manufacturing's "China shock" - but hitting knowledge workers. Especially entry-level, white-collar work could get automated. The key difference? "Winning the AI tech competition with other countries won't be enough. It's equally vital to win the battle to re-skill workers."

Digging into these big questions in this week’s AI in the News: https://fdaudens.substack.com/publish/posts/detail/160596301

Also, I'm curious: how are you keeping up with this pace of change? What strategies are working for you?
abidlabs 
posted an update 12 days ago
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JOURNEY TO 1 MILLION DEVELOPERS

5 years ago, we launched Gradio as a simple Python library to let researchers at Stanford easily demo computer vision models with a web interface.

Today, Gradio is used by >1 million developers each month to build and share AI web apps. This includes some of the most popular open-source projects of all time, like Automatic1111, Fooocus, Oobabooga’s Text WebUI, Dall-E Mini, and LLaMA-Factory.

How did we get here? How did Gradio keep growing in the very crowded field of open-source Python libraries? I get this question a lot from folks who are building their own open-source libraries. This post distills some of the lessons that I have learned over the past few years:

1. Invest in good primitives, not high-level abstractions
2. Embed virality directly into your library
3. Focus on a (growing) niche
4. Your only roadmap should be rapid iteration
5. Maximize ways users can consume your library's outputs

1. Invest in good primitives, not high-level abstractions

When we first launched Gradio, we offered only one high-level class (gr.Interface), which created a complete web app from a single Python function. We quickly realized that developers wanted to create other kinds of apps (e.g. multi-step workflows, chatbots, streaming applications), but as we started listing out the apps users wanted to build, we realized what we needed to do:

Read the rest here: https://x.com/abidlabs/status/1907886