Codex is Open Sourcing AI models

Building on our work to get Claude Code to train open source models, we are now getting Codex to go further. We gave Codex access to the Hugging Face Skills repository, which contains skills for Machine Learning and AI tasks such as training or evaluating models. With HF skills, a coding agent can:

Fine-tune and apply RL alignment on language models
Review, explain, and act on live training metrics from Trackio
Evaluate checkpoints and act on evaluation results
Create reports from experiments
Export to and quantize models with GGUF for local deployment
Publish models to the Hub

This tutorial dives even deeper and shows you how it works and how to use it yourself. So let's get started.

Codex uses AGENTS.md files to accomplish specialized tasks, whilst Claude Code uses 'Skills'. Fortunately, 'HF-skills' is compatible with both approaches and works with major coding agents like Claude Code, Codex, or Gemini CLI.

With HF-skills, you can tell Codex something like:

Fine-tune Qwen3-0.6B on the dataset open-r1/codeforces-cots

And Codex will:

Validate your dataset format
Select appropriate hardware (t4-small for a 0.6B model)
Use and update a training script with Trackio monitoring
Submit the job to Hugging Face Jobs
Report the job ID and estimated cost
Check on progress when you ask
Help you debug if something goes wrong

The model trains on Hugging Face GPUs while you do other things. When it's done, your fine-tuned model appears on the Hub, ready to use.

This isn't a toy demo. The extension supports the same training methods used in production: supervised fine-tuning, direct preference optimization, and reinforcement learning with verifiable rewards. You can train models from 0.5B to 7B parameters, convert them to GGUF for local deployment, and run multi-stage pipelines that combine different techniques.