LLM Tuning Patterns

What problem does it solve?

This Skill provides expert patterns and code examples for fine-tuning Large Language Models (LLMs), addressing the complexities of efficient training, dataset preparation, and model evaluation.

Core Features & Use Cases

• Efficient Fine-Tuning: Implement QLoRA and LoRA for memory-efficient training on consumer hardware.
• Dataset Preparation: Format instruction datasets and understand label masking for optimal training.
• Preference Alignment: Utilize Direct Preference Optimization (DPO) to align models with human preferences.
• Model Evaluation: Integrate with lm-evaluation-harness for standardized benchmarking.
• Use Case: Fine-tune a Llama-2 7B model for a specific task like customer support summarization using QLoRA, ensuring efficient use of GPU VRAM and achieving high performance.

Quick Start

Use the LLM Tuning Patterns skill to perform QLoRA fine-tuning on a Llama-2 7B model with the provided instruction dataset.