llm-finetuning

What problem does it solve?

This Skill provides expert guidance for fine-tuning large language models, enabling users to adapt powerful foundation models to specific domains and tasks efficiently.

Core Features & Use Cases

• Parameter-Efficient Fine-Tuning (PEFT): Expert advice on using LoRA and QLoRA to significantly reduce memory requirements while achieving high performance.
• Dataset Curation: Guidance on preparing high-quality, task-specific datasets for optimal training results.
• Training Optimization: Best practices for hyperparameter selection, evaluation strategies, and adapter deployment.
• Use Case: A researcher wants to fine-tune a large language model for medical text analysis. This Skill will guide them through preparing a medical dataset, configuring LoRA parameters, and optimizing the training process for accurate domain-specific outputs.

Quick Start

Consult the skill for advice on configuring LoRA with appropriate rank, alpha, and target modules for fine-tuning a language model.