fine-tuning-guide

What problem does it solve?

This Skill guides users through the complex process of fine-tuning Large Language Models (LLMs), helping them decide when it's necessary and how to do it effectively, thereby improving AI performance for specific tasks.

Core Features & Use Cases

• Decision Framework: Helps determine if fine-tuning is the right approach versus prompting or RAG.
• Data Preparation: Provides guidance on creating high-quality, diverse datasets for training.
• Training Methods: Details processes for OpenAI, Anthropic, and LoRA/QLoRA for open-source models.
• Quality Assessment: Offers strategies for evaluating the performance of fine-tuned models.
• Use Case: A company wants its AI assistant to consistently adopt a specific brand voice. This Skill helps them prepare data and choose the best fine-tuning method (e.g., LoRA) to achieve this.

Quick Start

Use the fine-tuning guide to understand when to fine-tune an LLM and how to prepare your data.