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peft

Fine-tune 7B-70B LLMs with LoRA and QLoRA on consumer GPUs.

247|22|Updated Dec 11, 2024
Search Tags:#memory optimization#lora#peft#qlora#llm fine-tuning#parameter-efficient#adapter training
One-click install
npx skills add https://github.com/graniet/kheish --skill peft-graniet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: peft
Source: https://github.com/graniet/kheish/tree/main/skills/mlops/training/peft
Command: npx skills add https://github.com/graniet/kheish --skill peft-graniet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Full fine-tuning of large language models with 7B to 70B parameters requires expensive high-memory GPUs and massive compute resources, making it inaccessible for most individual developers and small teams. This skill eliminates that barrier by enabling parameter-efficient fine-tuning methods that train less than 1% of model parameters, drastically reducing memory usage and compute costs while retaining nearly all of the accuracy of full fine-tuning.

Core Features & Use Cases

  • LoRA and QLoRA fine-tuning: Train lightweight adapters for 7B-70B parameter models on consumer GPUs like RTX 4090, or even 70B models on a single 24GB GPU using 4-bit quantization.
  • 25+ PEFT method support: Includes DoRA, AdaLoRA, IA3, Prefix Tuning, and other advanced parameter-efficient methods for different fine-tuning needs.
  • Multi-adapter serving: Deploy and switch between multiple task-specific adapters for a single base model at runtime, eliminating the need to store and load separate full fine-tuned models.
  • Use case example: A customer support team can fine-tune a Llama 3.1 70B base model on their support ticket data using QLoRA on a single consumer GPU, then serve specialized adapters for billing, technical, and account issues from the same base model.

Quick Start

Use the peft skill to fine-tune a Llama 3.1 8B model on your custom instruction dataset using LoRA with rank 16, then save the trained adapter for later deployment.

Frequently Asked Questions about peft

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

FAQPage Schema
How do I fine-tune a 70B parameter LLM on a single consumer GPU?▼

You can fine-tune a 70B parameter LLM on a single 24GB consumer GPU using QLoRA 4-bit quantization, which trains less than 1% of model parameters to drastically reduce memory usage and compute costs while retaining nearly all accuracy.

What is the difference between LoRA and QLoRA for LLM fine-tuning?▼

LoRA trains lightweight adapters for large language models on consumer GPUs, whereas QLoRA adds 4-bit quantization to further reduce memory requirements, enabling the fine-tuning of 70B parameter models on a single 24GB GPU.

Can I serve multiple task-specific adapters from a single base model?▼

Yes, multi-adapter serving allows you to deploy and switch between multiple task-specific adapters for a single base model at runtime, eliminating the need to store and load separate full fine-tuned models.

Which parameter-efficient fine-tuning methods are available besides LoRA?▼

Besides LoRA and QLoRA, parameter-efficient fine-tuning supports over 20 methods including DoRA, AdaLoRA, IA3, and Prefix Tuning, offering configurable rank, target modules, and quantization options for different needs.

Do I need expensive high-memory GPUs for parameter-efficient fine-tuning?▼

No, parameter-efficient fine-tuning eliminates the need for expensive high-memory GPUs by training less than 1% of model parameters, allowing 7B to 70B parameter models to be fine-tuned on consumer hardware like an RTX 4090.

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