peft-fine-tuning

What problem does it solve?

This Skill addresses the challenge of fine-tuning large language models (LLMs) efficiently, especially when computational resources like GPU memory are limited. It enables users to adapt powerful LLMs to specific tasks without the prohibitive cost and time of full model retraining.

Core Features & Use Cases

• Parameter-Efficient Fine-Tuning (PEFT): Utilizes techniques like LoRA and QLoRA to train only a small fraction of model parameters, drastically reducing memory and compute requirements.
• Memory Optimization: Allows fine-tuning of large models (7B-70B) on consumer-grade GPUs.
• Multi-Adapter Serving: Enables serving multiple fine-tuned variants of a single base model efficiently.
• Use Case: Fine-tune a 70B parameter LLM for a specialized medical domain on a single 24GB GPU, achieving high accuracy with minimal resource usage.

Quick Start

Install the PEFT library by running 'pip install peft bitsandbytes'.