TRL LLM Training
Write and debug code that post-trains language models with TRL (SFT, DPO, GRPO, KTO, reward models)
- Repository
- huggingface/skills
- Stars
- 11,253
- Licence
- Apache-2.0
- Files
- 1 file
What it does
Once installed, the agent knows how to write and fix training code for the TRL library, a Hugging Face toolkit for fine-tuning large language models. It can pick the right trainer for a dataset type (such as supervised fine-tuning, preference training, or reinforcement learning with reward functions), format data correctly, set memory and speed options, and use the trl command-line tool. It does not run training itself from this file alone; it guides the code the agent writes.
Great for
- Machine learning engineers fine-tuning open models with Hugging Face tools
- Developers debugging TRL trainer errors or dataset format problems
- Teams choosing between SFT, DPO, GRPO and KTO for a new training project
Skip it if
- People who only want to use a chat model and have no training code to write (the skill adds nothing for ordinary use)
- Projects needing stable APIs in the experimental trainers, which the file says may change without notice
Try asking
- “Write an SFT script with TRL that fine-tunes Qwen2.5-0.5B on a conversational dataset with LoRA”
- “Convert my preference pairs into the right format for DPOTrainer and debug the error I'm seeing”
- “Set up a GRPO run with a reward function that scores completion length”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 1 original files, the Apache-2.0 licence and an attribution note.
trl-training.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip trl-training.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip trl-training.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- State your dataset type up front (conversational, prompt-completion, preference pairs) so the agent picks the correct trainer and format
- Review generated training code before running it on paid GPU time, since the skill's examples download public models and datasets from the Hugging Face Hub
- Check the linked TRL documentation for current argument names, as the skill points to it for dataset formats and experimental trainers
What's inside
- SKILL.md
Redistributed unmodified from huggingface/skills under the Apache-2.0 licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.