Hugging Face

🤗💚 https://blogs.nvidia.com/blog/nvidia-to-acquire-hugging-face/

We use Inkling-Small to turn paper abstracts into quick, useful summaries. open weights × open science 🤝

We've just surpassed 3 million models on the Hub 🤗 the community is accelerating towards an open, distributed future where open AI is everywhere, for everyone 🚀

The State of Open Models, Summer 2026 ☀️ frontier models are getting larger, but small models still dominate real-world usage. Qwen leads local inference, followed by Gemma. AI agents are becoming a major force on the Hub Full picture on the blog 🤗 https://huggingface.co/blog/state-of-open-models-summer-2026

Training Agents 4: From reward functions to environments. https://x.com/i/broadcasts/1OxwbngOoXEJB

The same model, with the same weights, scores 62% in one agent harness and 33% in another. @adithya_s_k and the @huggingface team just released the ultimate guide to multi-harness RL, and it's one of the most practical RL write-ups this year, and everything open! The trick is simple. Don't touch the harness. Point it at a proxy instead of the model. The proxy speaks all four API formats coding agents use (OpenAI Chat Completions, OpenAI Responses, Anthropic Messages, Gemini). It records the exact token ids and logprobs vLLM sampled, and you train on that. You don't change a single line of Claude Code, Codex or OpenCode. Results: 🔹 Trained across 4 harnesses at once, LFM2.5-2.6B by @liquidai went from 42% to 54% 🔹 31% fewer tool calls, thanks to a small bonus for solving tasks in fewer steps 🔹 Training in OpenCode alone took OpenCode from 34% to 58%, but the multi-harness model improved everywhere They also tried the shortcut everyone reaches for: fine-tune on 3,189 successful rollouts from Qwen3.8-27B. Imitation plateaued at 47.5%, below both RL runs. Copying a bigger model doesn't get you there. Practice does. The best part is that everything is open: the capture proxy in OpenEnv, the trainer in TRL, the tasks, the SFT data, the training code and all seven trained models. Agents will run in dozens of harnesses. Now open models can be trained for each of them, by anyone. Read it here 👇 https://huggingface.co/spaces/FineEnvs/multi-harness-rl

What would you like to see in Gradio? Drop your feature requests below 👇

MCPs are making quite the comeback! You can now use them to bring your data to https://hf.co/chat?mode=ml-intern

We sat down with @MistralAI's Pierre Stock and Sagar Vaze from Mistral on launch day of Mistral Large 4. Open weights. Trained in Mistral’s own data centers. Excels at cyber. Full conversation on cyber, training, evals and sandboxing: https://youtu.be/FczbSauVvjw

LIVE STREAM: New Open Weight Model https://x.com/i/broadcasts/1DxleVaPLagKL

Even the most regulated industries are going open-source 🤗

Local AI 202: Get Massive Speed-ups with Speculative Decoding https://x.com/i/broadcasts/1NGaroZveLVJj