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peft-fine-tuning

Automate parameter-efficient fine-tuning of large language models with LoRA and QLoRA adapters.

27|2|Updated Jan 15, 2024
Search Tags:#llm#fine-tuning#adapter#lora#transformers#peft#qlora
One-click install
npx skills add https://github.com/erfanzar/Xerxes-Agents --skill peft-fine-tuning-erfanzar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: peft-fine-tuning
Source: https://github.com/erfanzar/Xerxes-Agents/tree/main/src/python/xerxes/skills/training/peft
Command: npx skills add https://github.com/erfanzar/Xerxes-Agents --skill peft-fine-tuning-erfanzar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Fine-tune large language models efficiently using parameter-efficient methods like LoRA and QLoRA, enabling high-quality results with far fewer trainable parameters.

Core Features & Use Cases

  • PEFT methods include LoRA, QLoRA, AdaLoRA, IA3, and more for memory-efficient fine-tuning.
  • Ideal for 7B-70B models on consumer GPUs, multi-adapter deployment, and rapid experimentation across tasks.
  • Use cases include adapting base models to domain-specific tasks, injecting new capabilities, or creating task-specific variants.

Quick Start

Install the required Python packages and run a PEFT-based fine-tuning workflow to apply adapters to a base model.

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 a large language model on a consumer GPU without running out of memory?▼

LoRA and QLoRA are parameter-efficient fine-tuning methods that attach small trainable adapter matrices to frozen model weights, reducing trainable parameters while maintaining performance for domain-specific adaptation.

Can I use different adapters like IA3 and AdaLoRA for fine-tuning a single base model?▼

Yes, this supports configuring multiple PEFT methods including LoRA, QLoRA, AdaLoRA, and IA3, enabling rapid experimentation and multi-adapter deployment across different tasks on the same base model.

Do I need bitsandbytes to run QLoRA fine-tuning on transformers models?▼

Bitsandbytes is an optional dependency for quantization strategies in QLoRA, while a Python environment with peft, transformers, and torch is fundamentally required to execute the parameter-efficient fine-tuning workflows.

What is the best way to inject new capabilities into a 70B model without full parameter training?▼

Using parameter-efficient fine-tuning with adapters like LoRA injects new capabilities into 70B models by training only a fraction of parameters, avoiding full parameter training while achieving high-quality task-specific variants.

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