model_finetuning

What problem does it solve?

This Skill addresses the challenge of adapting pre-trained Large Language Models (LLMs) to specific tasks or human preferences, improving their performance and alignment beyond general capabilities.

Core Features & Use Cases

• Instruction Tuning (SFT): Fine-tune models to follow instructions effectively.
• Preference Alignment (DPO): Align model outputs with human preferences using Direct Preference Optimization.
• Reward Optimization (PPO/GRPO): Train models using reinforcement learning to maximize a reward signal, suitable for complex tasks or human feedback.
• Reward Model Training: Develop models that can score the quality of LLM generations.
• Use Case: You have a base LLM and want it to generate more helpful and harmless responses according to user feedback. This Skill provides the tools to fine-tune the model using techniques like SFT for instruction following and DPO for preference alignment.

Quick Start

Use the model_finetuning skill to fine-tune a Qwen2.5-0.5B model using SFT with the provided dataset.