What is the main benefit of using PEFT over full fine-tuning?

PEFT allows you to fine-tune large models by training less than 1% of the parameters, significantly reducing GPU memory requirements and storage costs while maintaining comparable accuracy to full fine-tuning.

How do I merge a LoRA adapter back into the base model?

The skill includes code patterns for using the 'merge_and_unload()' method, which combines the adapter weights with the base model for faster inference and deployment without adapter overhead.

Can I run QLoRA on a single consumer GPU?

Yes, using QLoRA with 4-bit quantization, you can fine-tune 70B parameter models on a single 24GB VRAM GPU, such as an NVIDIA RTX 3090 or 4090.

Which model architectures does this skill support?

This skill provides configuration patterns for Llama (including 3.1), Mistral, Qwen, Falcon, BLOOM, and various GPT architectures.

What is the difference between LoRA and QLoRA?

LoRA adds small trainable rank-decomposition matrices to the model, while QLoRA further optimizes memory by quantizing the base model to 4-bit precision during the training process.

PEFT LLM Fine-Tuning

Name: PEFT LLM Fine-Tuning
Author: Orchestra-Research

byOrchestra-Research

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3,983

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Data Science & ML

Optimizes large language model fine-tuning using LoRA, QLoRA, and other parameter-efficient methods to significantly reduce memory and hardware requirements.

This skill provides specialized implementation patterns for Parameter-Efficient Fine-Tuning (PEFT), enabling the adaptation of large language models (7B to 70B+ parameters) on consumer-grade hardware. It offers comprehensive guidance on Low-Rank Adaptation (LoRA) and QLoRA, providing optimized configurations for various architectures like Llama 3.1 and Mistral. By focusing on training less than 1% of total parameters, this skill helps developers iterate faster, reduce storage costs via tiny adapter files, and implement multi-adapter serving for diverse production tasks.

Key Features

01Memory-efficient 4-bit quantization strategies using bitsandbytes

02Support for 25+ adapter methods including IA3, AdaLoRA, and Prefix Tuning

03Automated rank (r) and alpha scaling optimization for various model sizes

04Dynamic multi-adapter serving and runtime switching capabilities

053,983 GitHub stars

06Standardized LoRA and QLoRA implementation patterns for HuggingFace Transformers

Use Cases

01Creating lightweight, task-specific adapters (6MB) instead of full model checkpoints (14GB+)

02Fine-tuning 70B parameter models on a single 24GB consumer GPU

03Deploying a single base model that serves multiple specialized fine-tuned variants simultaneously

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add orchestra-research/ai-research-skills peft

For use in Claude.ai and ChatGPT

Download Skill