Fine Tuning with TRL
Skill Verified ActiveFine-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.
To enable users to fine-tune LLMs using advanced reinforcement learning techniques with TRL for instruction tuning, preference alignment, and reward optimization.
Features
- Supervised Fine-Tuning (SFT) for instruction tuning
- Direct Preference Optimization (DPO) for preference alignment
- Proximal Policy Optimization (PPO) for reward optimization
- Group Relative Policy Optimization (GRPO) for memory-efficient RL
- Reward model training for RLHF pipelines
- Code examples for various fine-tuning workflows
Use Cases
- Aligning LLMs with human preferences using preference data
- Training LLMs to follow instructions effectively
- Optimizing LLM policies with reinforcement learning
- Building full RLHF pipelines
Non-Goals
- Basic fine-tuning without RL (use HuggingFace Trainer)
- YAML-based training configuration (use Axolotl)
- Educational, minimal fine-tuning (use LitGPT)
- Fast LoRA training (use Unsloth)
Prerequisites
- Python environment
- HuggingFace Transformers and TRL libraries
- PyTorch (or other compatible deep learning framework)
- GPU with sufficient VRAM (depending on model size and method)
Documentation
- info:Configuration & parameter referenceWhile the SKILL.md provides configuration examples for various TRL methods (e.g., SFTConfig, DPOConfig), it doesn't explicitly document all possible parameters or their default values for each configuration object. Specific hyperparameter tables offer guidance but lack comprehensive documentation.
Code Execution
- info:Error HandlingThe provided code examples demonstrate standard Python library usage; while the underlying TRL library likely has robust error handling, the examples themselves do not explicitly showcase custom error handling or structured error reporting for user-facing errors.
Execution
- info:Pinned dependenciesThe SKILL.md lists dependencies but does not specify pinned versions or provide a lockfile. The README suggests installation via `pip`, which can install latest versions by default without pinning.
Practical Utility
- info:Edge casesThe skill addresses common issues like OOM errors and poor alignment quality in DPO training, offering specific parameter adjustments and recovery steps. However, it doesn't systematically list all potential failure modes and recovery steps for every scenario.
Installation
npx skills add davila7/claude-code-templatesRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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