peft-fine-tuning

Name: peft-fine-tuning
Availability: InStock
Author: unclehowell

Fine-tune large language models using LoRA or QLoRA with 4-bit quantization.

Updated Sep 1, 2021

Search Tags:#fine-tuning #memory optimization #large language models #lora #peft #qlora #parameter-efficient

Ready to run

npx skills add https://github.com/unclehowell/unclehowell --skill peft-fine-tuning-unclehowell

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: peft-fine-tuning
Source: https://github.com/unclehowell/unclehowell/tree/main/skills/mlops/training/peft
Command: npx skills add https://github.com/unclehowell/unclehowell --skill peft-fine-tuning-unclehowell

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Fine‑tuning large language models often requires extensive GPU memory and long training times because full‑parameter updates are costly. This Skill enables parameter‑efficient fine‑tuning, reducing compute and memory needs while preserving model performance.

Core Features & Use Cases

LoRA & QLoRA support for models from 7B to 70B parameters.
Multi‑adapter management to serve multiple task‑specific versions from a single base model.
Memory‑optimized training using 4‑bit quantization and gradient checkpointing.
Integration examples with Transformers, Accelerate, TRL, Axolotl, and vLLM for both training and inference.

Quick Start

Invoke the peft-fine-tuning skill to adapt a target LLM using LoRA on your dataset.

Frequently Asked Questions about peft-fine-tuning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I fine-tune large language models without running out of GPU memory?▼

Parameter-efficient fine-tuning reduces GPU memory needs by training a small fraction of parameters. This approach uses LoRA and QLoRA adapters to update models efficiently while preserving performance.

Can I use 4-bit quantization to train 70B parameter models on a single GPU?▼

4-bit quantization enables memory-efficient training for 70B parameter models on a single GPU. It reduces memory consumption significantly by lowering parameter precision during the fine-tuning process.

What's the best way to manage multiple task-specific versions from one base model?▼

Multi-adapter management allows you to serve multiple task-specific versions from a single base model. This technique applies LoRA or QLoRA adapters to adapt the base model for different tasks efficiently.

How does QLoRA fine-tuning differ from full-parameter training for large language models?▼

QLoRA fine-tuning trains a small fraction of parameters using 4-bit quantization, unlike full-parameter training which updates all weights. This reduces compute and memory requirements while maintaining model performance.

Does this parameter-efficient fine-tuning approach work with vLLM and Axolotl?▼

This parameter-efficient fine-tuning approach integrates directly with frameworks including vLLM, Axolotl, TRL, and Accelerate. It supports both the training and inference phases of large language models.

When do I need LoRA or QLoRA adapters for large language models?▼

LoRA or QLoRA adapters are needed when full-parameter fine-tuning is too costly in compute and memory. They enable parameter-efficient updates for models ranging from 7B to 70B parameters across single or multi-GPU setups.