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.

We explored this single prompt approach in the Claude Code tutorial. However, we can now go further and get OpenAI Codex to do end-to-end Machine Learning experiments. For example, Codex should be able to monitor progress, evaluate the models, and maintain an up to date training report. This will allow engineers to delegate experiments to Codex and review reports in a more hands-off way. It will also allow Codex to make more decisions on its own based on the training report and evaluation results.

Before starting, you'll need:

• A Hugging Face account with a Pro or Team / Enterprise plan
• A write-access token from huggingface.co/settings/tokens
• Codex installed and configured

Clone the repository:

git clone https://github.com/huggingface/skills.git
cd skills

Codex will automatically detect the AGENTS.md file in the repository and load the skills. You can configure the Hugging Face MCP server for additional Hub integration capabilities by adding it to your ~/.codex/config.toml file.