Hugging Face

We got Claude Code to train an open LLM 🤯 Not just to write the training script, but to actually submit jobs to cloud GPUs, monitor progress, and push finished models to the Hugging Face Hub. Just say something like: "Fine-tune Qwen3-0.6B on the dataset open-r1/codeforces-cots" This is using Skills: packaged instructions, scripts, and domain knowledge, to accomplish specialized tasks: - You tell the agent to fine-tune a model on a dataset. You can define the dataset or let it search. - Agent picks hardware based on model size and checks dataset. - Job runs on cloud gpus. Either main run, or test run. - Agent share real-time progress dashboard with Trackio. - Checkpoints are pushed to HF. Check out the full tutorial ⤵️

I'm super excited to see evals becoming more native in the Hub 🤗 We just integrated the Hugging Face filesystem into Inspect AI, and honestly? It's wild that we've been paying AWS to share evaluation logs when the Hub exists. ⚙️ The entire flow: 1. Run your eval, log to a HF dataset: inspect eval inspect_evals/bfcl \ --model hf-inference-providers/moonshotai/Kimi-K2-Thinking \ --log-dir hf://datasets/username/eval-logs \ --log-shared \ --log-buffer 100 2. Watch results locally: inspect view --log-dir hf://datasets/username/eval-logs 3. Or duplicate this Space to browse and share on the Hub: dvilasuero/inspect_viewer_template That's it. No S3 buckets, no access keys, no monthly AWS bills. ⁉️ Why this matters: - Free: HF datasets don't cost a cent. S3 adds up fast at scale - Open by default: Eval logs live where your models live. Reproducible, verifiable, forkable The eval ecosystem shouldn't run through a single cloud provider's billing system. Making evaluation as native to the Hub as model sharing feels like the obvious move. Works with the latest Inspect release. Give it a shot 🚀 See the first comment for a Space with eval results on the Hub

We used Claude Code to train open LLMs! We plugged HF skills into claude code and it was able to train LLMs end-to-end. Best thing, this works on all major coding agents: Codex, Cursor, and Gemini CLI. 🔗 Deep dive blog https://lnkd.in/eX3UQqH9 - You tell the agent to fine-tune a model on a dataset. You can define the dataset or let it search. - Agent picks hardware based on model size and checks dataset. - Job runs on cloud gpus. Either main run, or test run. - Agent share real-time progress dashboard with Trackio. - Checkpoints are pushed to the hub. Take it for a whirl now in your fav coding agent.

NEW RELEASE - huggingface/skills is a universal implementation of agent context for AI tasks like training models, building datasets, and generating datasets. - compatible with all major coding agent tools: Codex, Cursor, Claude Code, Gemini CLI. - has integrated local script and remote long running cloud jobs. - swaps in with skills, AGENTS.md, extensions. whatever you want to call it. - add relevant MCP servers like the Hugging Face MCP In practice, skills wraps around a skill based on Claude Code’s standard with instructions, resources, scripts, and examples. But it exposes entry points to all other utilities as AGENTS.md or gemini extensions.

Transformers v5's first release candidate is out 🔥 The biggest release of my life. It's been five years since the last major (v4). From 20 architectures to 400, 20k daily downloads to 3 million. The release is huge, with tokenization (no slow tokenizers!), modeling (improved vLLM & SGLang compatibility), and processing. We wrote some very detailed release notes over on GitHub. A release candidate is only the first step. We'll iterate fast over the following RCs, paving the way to a very robust v5. Thanks a lot to all of our contributors, as well as PyTorch, vLLM, SGLang, ggml, Awni Hannun (MLX), Unsloth AI, Axolotl and many others for the help along the way.

“Universities need to understand real industry use cases” that’s what I heard yet again last Wednesday during a talk at Adopt AI - Grand Palais (Future-ready talent: bridging the gap between Domain and AI), a line I’ve heard far too many times already. After speaking with several students throughout the day, it became clear that academia sadly still remains quite distant from the tools that actual AI builders rely on. ⭐️Today, I’m happy to share that Université de Toulouse is among the first institutions taking a concrete step toward closing that gap by joining Hugging Face’s new Academia Hub program (https://lnkd.in/egvxpSmN )⭐️ For students, bridging this gap starts with being exposed to the tools and workflows professionals use daily, and exploring the Hub’s extensive libraries of models, datasets, and Spaces is a simple, meaningful way to stay connected to real industry practice. For researchers, it means having shared workspaces, reproducibility, robust versioning, and access to compute without the usual friction that slows progress. 💡That’s why we created Academia Hub this year: to offer universities access to advanced Hub features at an academic rate. Institutions benefit from built-in compute credits, priority GPU access, expanded storage, enterprise-grade account management, and the ability to publish community blogs to increase the visibility of their work. We’re excited to support the IAFA master’s program and researchers across Université de Toulouse as they explore, iterate, and stay curious with tools that don’t limit them, and we look forward to welcoming more early adopters soon.🤗 Thank you to Jose G Moreno and his team for making this partnership possible! If you’re interested to learn more about the program, send me a DM!

Brush up your next-level coding agent skills, now! Why use december to relax with loved ones, join jovial elf related coding challenges, or brush up on obscure programming languages? It's time to get real! In this hackathon, we will solve open AI for all humanity. No elves, just agent context and PRs. We’re taking the latest coding agents tools (claude code, codex, cursor, gemini-cli) and using them to collectively level up open source AI with real contributions. We've created a new tool especially for this: hf-skills, which contains prepackaged agent context for each quest; like model evals, creating datasets, and training models. Each week is a new quest where we use the skills (AGENTS(.)md/extensions) to solve a problem in open source AI. Winners will be rewarded for their real contributions, and ranked on a leaderboard. Check out the repo to get involved: https://lnkd.in/e-TVQZZE