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peft-fine-tuning

Train LoRA and QLoRA adapters for large language models with PEFT.

4|Updated May 18, 2026
Search Tags:#lora#peft#qlora#llm fine-tuning#huggingface transformers#adapter training#low vram training
One-click install
npx skills add https://github.com/ZardLi1115/zedclaw --skill peft-fine-tuning-zardli1115
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: peft-fine-tuning
Source: https://github.com/ZardLi1115/zedclaw/tree/main/optional-skills/mlops/peft
Command: npx skills add https://github.com/ZardLi1115/zedclaw --skill peft-fine-tuning-zardli1115

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires peft, transformers, torch, bitsandbytes, and includes references (resource) components.

What problem does it solve?

Fine-tuning large language models is often too expensive and memory-intensive to do with full-parameter training, especially on limited GPUs.

Core Features & Use Cases

  • Parameter-efficient fine-tuning (PEFT): Train only a small fraction of model parameters using adapter methods such as LoRA, QLoRA, and other PEFT variants.
  • Memory-optimized strategies: Use LoRA for general efficiency and QLoRA (LoRA + 4-bit quantization via bitsandbytes) to fit very large models (e.g., 70B) on constrained VRAM.
  • Adapter workflows: Support multi-adapter training and serving (multiple task-specific adapters, runtime switching, and optional merging for deployment).
  • Use Case: Adapt a 7B–70B instruction model to your domain (or multiple domains) while keeping training costs low by training <1% of parameters via adapters.

Quick Start

Use the peft-fine-tuning skill to fine-tune your base LLM with LoRA or QLoRA by configuring target modules, loading your dataset, and training an adapter with HuggingFace Transformers and PEFT.

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 with low GPU memory?▼

You can fine-tune large language models with low GPU memory by training adapter modules like LoRA or QLoRA instead of updating all weights, which trains under 1% of parameters to significantly reduce VRAM requirements.

Can I train a 70B model on constrained VRAM using QLoRA?▼

Yes, you can train a 70B model on constrained VRAM using QLoRA, which combines LoRA adapter training with 4-bit quantization via bitsandbytes to fit very large models on limited hardware.

What is the difference between LoRA and QLoRA for adapter training?▼

LoRA provides general parameter-efficient fine-tuning efficiency, while QLoRA adds 4-bit quantization via bitsandbytes to enable training very large models like 70B parameters under strict limited GPU memory constraints.

How do I manage multiple task-specific adapters for runtime switching?▼

You can manage multiple task-specific adapters for runtime switching using the PEFT and Transformers stack, which supports multi-adapter training, serving, and optional merging for deployment.

Do I need bitsandbytes to perform parameter-efficient fine-tuning?▼

You do not need bitsandbytes for standard LoRA adapter training, but it is required as an optional dependency to enable QLoRA 4-bit quantization for fitting very large models on constrained VRAM.

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