fine-tuning-with-trl
CommunityAlign LLMs with human preferences.
Authorkwasi-cpu
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill addresses the challenge of aligning Large Language Models (LLMs) with human preferences and instructions, making them more helpful, harmless, and honest.
Core Features & Use Cases
- Supervised Fine-Tuning (SFT): Instruction tuning for better response generation.
- Preference Alignment (DPO): Directly optimize models using preference data without a separate reward model.
- Reinforcement Learning (PPO/GRPO): Further refine models using reinforcement learning techniques for advanced alignment.
- Reward Model Training: Train models to score the quality of LLM generations.
- Use Case: You have a base LLM and want to make it better at following user instructions and adhering to specific safety guidelines. This Skill provides the tools to fine-tune the model using various techniques, including human feedback.
Quick Start
Use the fine-tuning-with-trl skill to perform supervised fine-tuning on a base model using the provided dataset.
Dependency Matrix
Required Modules
trltransformersdatasetspeftacceleratetorch
Components
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: fine-tuning-with-trl Download link: https://github.com/kwasi-cpu/hermes-agent/archive/main.zip#fine-tuning-with-trl Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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