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peft-fine-tuning

Trains less than 1% of LLM weights using LoRA, QLoRA, and 25+ PEFT methods with HuggingFace Transformers, TRL, and vLLM integration.

1|Updated Jun 25, 2026
Search Tags:#llm#fine-tuning#adapters#lora#peft#qlora#parameter-efficient
One-click install
npx skills add https://github.com/Signmanal/VIGIL --skill peft-fine-tuning-signmanal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: peft-fine-tuning
Source: https://github.com/Signmanal/VIGIL/tree/main/optional-skills/mlops/peft
Command: npx skills add https://github.com/Signmanal/VIGIL --skill peft-fine-tuning-signmanal

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Fine-tuning large language models (7B-70B parameters) traditionally requires massive GPU memory and compute resources that are cost-prohibitive for many 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, compute costs, and training time.

Core Features & Use Cases

  • 25+ PEFT Method Support: Includes LoRA, QLoRA, DoRA, IA3, Prefix Tuning, and AdaLoRA for different quality, speed, and memory trade-offs.
  • Memory-Optimized Training: Fine-tune 70B parameter models on a single 24GB consumer GPU using 4-bit quantization via QLoRA.
  • Multi-Adapter Serving: Deploy multiple task-specific fine-tuned variants from a single base model without duplicating the full model weights.
  • Use Case: A security research team can fine-tune a 70B threat analysis model on a local workstation, then serve separate adapters for malware analysis, phishing detection, and threat intelligence summarization without managing multiple full model copies.

Quick Start

Use the peft-fine-tuning skill to fine-tune a 7B Llama model on your custom instruction dataset using LoRA with 4-bit quantization to run training on a 24GB GPU.

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 on a single consumer GPU?▼

Fine-tuning large language models on a single 24GB consumer GPU is possible using parameter-efficient methods like QLoRA. This approach applies 4-bit quantization to train less than 1% of model weights, drastically reducing memory barriers for 7B-70B parameter models.

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

LoRA and QLoRA are both parameter-efficient fine-tuning methods that train a small percentage of weights. QLoRA specifically adds 4-bit quantization on top of LoRA, enabling the fine-tuning of massive 70B parameter models on a single 24GB GPU by further compressing memory usage.

Can I deploy multiple fine-tuned variants from a single base model?▼

Multi-adapter serving allows you to deploy multiple task-specific fine-tuned variants from a single base model. This prevents duplicating full model weights, letting you serve separate adapters for tasks like malware analysis and phishing detection efficiently.

Does parameter-efficient fine-tuning work with HuggingFace Transformers and vLLM?▼

Parameter-efficient fine-tuning integrates directly with the HuggingFace Transformers, TRL, and vLLM ecosystems. This compatibility ensures that trained adapters can be seamlessly merged and deployed within standard model serving frameworks.

Which PEFT methods are available for memory-optimized LLM training?▼

Over 25 parameter-efficient fine-tuning methods are supported for memory-optimized LLM training. These include LoRA, QLoRA, DoRA, IA3, Prefix Tuning, and AdaLoRA, offering various trade-offs between training quality, speed, and memory consumption.

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