How do I choose the right Rank (r) for LoRA?

A rank of 8-16 is the recommended starting point for general fine-tuning. Higher ranks (32-64) provide more capacity for complex domain adaptation but increase memory usage.

Is it possible to merge adapters back into the base model?

Yes, this skill provides implementation patterns to merge and unload adapters, allowing you to deploy the fine-tuned model as a standalone unit without adapter overhead.

What is the main benefit of using PEFT over full fine-tuning?

PEFT allows you to fine-tune large models by training less than 1% of the total parameters, which significantly reduces GPU memory requirements, speeds up training, and produces tiny adapter files (MBs vs GBs).

Can I fine-tune a 70B model on a single consumer GPU?

Yes, using the QLoRA (4-bit quantization) patterns included in this skill, you can fine-tune 70B parameter models on a single 24GB VRAM GPU like an RTX 3090 or 4090.

PEFT Fine-Tuning

Name: PEFT Fine-Tuning
Author: zechenzhangAGI

byzechenzhangAGI

•

384

데이터 과학 및 ML

Fine-tunes large language models using LoRA, QLoRA, and other parameter-efficient methods to drastically reduce memory and compute requirements.

소개

The PEFT Fine-Tuning skill provides a comprehensive framework for implementing Parameter-Efficient Fine-Tuning techniques within the Claude Code environment. By training less than 1% of a model's parameters using methods like LoRA and QLoRA, this skill enables developers to adapt large-scale models (7B to 70B) on consumer-grade hardware. It includes production-ready implementation patterns for multi-adapter serving, 4-bit quantization, and seamless integration with the Hugging Face Transformers ecosystem, making it essential for AI researchers and engineers optimizing model performance under resource constraints.

주요 기능

Memory-efficient 4-bit quantization for training 70B models on 24GB GPUs
Integration patterns for TRL, Axolotl, and vLLM inference
Supports 25+ adapter methods including LoRA, QLoRA, IA3, and Prefix Tuning
Standardized configuration for attention and MLP layer targeting across architectures
Dynamic multi-adapter management and runtime switching capabilities
384 GitHub stars

사용 사례

Fine-tuning Llama 3 or Mistral models on local consumer GPUs like the RTX 4090
Creating and serving multiple task-specific adapters from a single base model
Adapting massive models to niche domains with minimal accuracy loss and storage overhead

소개

주요 기능

Memory-efficient 4-bit quantization for training 70B models on 24GB GPUs
Integration patterns for TRL, Axolotl, and vLLM inference
Supports 25+ adapter methods including LoRA, QLoRA, IA3, and Prefix Tuning
Standardized configuration for attention and MLP layer targeting across architectures
Dynamic multi-adapter management and runtime switching capabilities
384 GitHub stars

사용 사례

Fine-tuning Llama 3 or Mistral models on local consumer GPUs like the RTX 4090
Creating and serving multiple task-specific adapters from a single base model
Adapting massive models to niche domains with minimal accuracy loss and storage overhead