Using Codex to Generate Synthetic Training Pairs for Fine-tuning Qwen2.5

Melvin Vivas · X post · 2026-09-14 · Open on X

Topics: Fine-tuning & Model Customization, AI Dev Tools & Productivity · Level: intermediate

Summary

Melvin Vivas shows that a coding agent (OpenAI Codex) can handle dataset prep for machine learning. Here it generated synthetic input/output training pairs so a small Qwen2.5 model could be fine-tuned.

Key points

• A coding agent like Codex can write the scripts that prepare fine-tuning datasets.
• Synthetic data can fill gaps when you don't have enough real training pairs.
• The target was a small Qwen2.5 model. The post says '2b', but Qwen2.5 sizes are 0.5B, 1.5B, 3B and up, so check the exact checkpoint.
• Fine-tuning data is usually formatted as instruction/response pairs.

Resources mentioned

• OpenAI Codex · tool · openai.com · paid
  OpenAI's coding agent. In the diagram it writes code, fixes review findings and drives the build loop. The creator also used it to make this video.
  Also in: An agent bot that installs and drives Codex on its own (Melvin Vivas on X · notes), Asking a Coder bot to install Codex (Melvin Vivas on X · notes), Sign in with ChatGPT: Setting Usage Limits for Each App (Melvin Vivas on X · notes), Codex Cloud Environments Must Be Saved & Published Before Use (Melvin Vivas on X · notes) and 240 more
• Qwen2.5 · tool · github.com · free
  Alibaba's family of open-weight LLMs in many sizes, often used as fine-tuning bases.

Try this

• Try asking a coding agent to generate and format synthetic training pairs for your fine-tuning task.
• Fine-tune a small Qwen2.5 model on synthetic task-specific training pairs generated by a coding agent.