Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "trl" skill in my project. Repository: https://github.com/majiayu000/claude-skill-registry Please read the repo to find the SKILL.md file(s), then: 1. Download them into the correct skills directory (.claude/skills/ or .cursor/skills/) 2. Include any companion files referenced by the skill 3. Confirm what was installed and where
Description
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
Overview
Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub. TRL provides multiple training methods: • SFT (Supervised Fine-Tuning) - Standard instruction tuning • DPO (Direct Preference Optimization) - Alignment from preference data • GRPO (Group Relative Policy Optimization) - Online RL training • Reward Modeling - Train reward models for RLHF For detailed TRL method documentation: `python hf_doc_search("your query", product="trl") hf_doc_fetch("https://huggingface.co/docs/trl/sft_trainer") # SFT hf_doc_fetch("https://huggingface.co/docs/trl/dpo_trainer") # DPO
Prerequisites Checklist
Before starting any training job, verify:
✅ **Dataset Requirements**
• Dataset must exist on Hub or be loadable via datasets.load_dataset() • Format must match training method (SFT: "messages"/text/prompt-completion; DPO: chosen/rejected; GRPO: prompt-only) • ALWAYS validate unknown datasets before GPU training to prevent format failures (see Dataset Validation section below) • Size appropriate for hardware (Demo: 50-100 examples on t4-small; Production: 1K-10K+ on a10g-large/a100-large)
Approach 2: TRL Maintained Scripts (Official Examples)
TRL provides battle-tested scripts for al
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