fine-tuning-with-trl

8.7

315

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

fine-tuning

8.7

Rating

Installs

AI & LLM

Quick Review

Excellent TRL fine-tuning skill with comprehensive coverage of RLHF methods (SFT, DPO, PPO, GRPO, reward modeling). The description clearly conveys when to use this skill, and SKILL.md provides complete, runnable code for three well-structured workflows with copy-paste checklists. Task knowledge is outstanding with practical examples, CLI alternatives, troubleshooting, and hardware guidance. Structure is clean with a logical progression and proper use of reference files for advanced topics. Novelty is strong—RLHF pipelines are complex multi-step processes that would require significant tokens and expertise from a CLI agent alone. Minor room for improvement: could slightly expand the 'when to use vs alternatives' section with more decision criteria, and the description could mention the three main workflows explicitly. Overall, this is a highly useful, well-documented skill that meaningfully reduces complexity for preference alignment tasks.