How do I deploy a LoRA-trained model?

You can either load the small adapter weights at runtime using the PEFT library or merge the LoRA weights directly into the base model for a standalone production deployment.

Does this skill support QLoRA?

Yes, it includes detailed patterns and configurations for 4-bit quantization (QLoRA), enabling the fine-tuning of large models like Llama 3 on limited VRAM.

Can I use multiple adapters simultaneously?

Yes, the skill provides implementation patterns for loading, switching between, and disabling multiple task-specific adapters on a single shared base model.

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

LoRA drastically reduces memory and storage requirements by training only ~0.1% of the model's parameters, allowing for faster training and the ability to run on consumer hardware while maintaining performance.

Which model architectures are supported?

The skill provides target module configurations for major transformer architectures including Llama, Mistral, Qwen, GPT-2, BERT, RoBERTa, Falcon, and Phi.

LoRA Fine-Tuning Expert

Name: LoRA Fine-Tuning Expert
Author: itsmostafa

byitsmostafa

•

Data Science & ML

Implements parameter-efficient fine-tuning using Low-Rank Adaptation (LoRA) to specialize large language models with minimal resource overhead.

About

This skill provides specialized guidance for implementing Low-Rank Adaptation (LoRA) and QLoRA to fine-tune large language models efficiently. It helps developers optimize GPU memory usage, manage rank configurations, target specific transformer layers, and handle the complete lifecycle of adapter training—from initial setup and configuration to merging weights for production deployment. Whether you are building task-specific adapters or training large models on consumer hardware, this skill offers the implementation patterns and best practices needed for efficient model specialization and LLM engineering.

Key Features

Full QLoRA (4-bit quantization) support for memory-constrained environments
Efficient parameter management reducing trainable weights to ~0.1%
Multi-adapter management for task switching without reloading base models
Best practices for rank (r) and alpha scaling optimization
10 GitHub stars
Pre-configured target modules for popular architectures like Llama, Mistral, and BERT

Use Cases

Creating specialized domain-specific adapters for a single base model
Optimizing LLM performance for specific tasks like classification or chat
Fine-tuning large models (7B+) on consumer-grade GPUs using QLoRA

About

Key Features

Full QLoRA (4-bit quantization) support for memory-constrained environments
Efficient parameter management reducing trainable weights to ~0.1%
Multi-adapter management for task switching without reloading base models
Best practices for rank (r) and alpha scaling optimization
10 GitHub stars
Pre-configured target modules for popular architectures like Llama, Mistral, and BERT

Use Cases

Creating specialized domain-specific adapters for a single base model
Optimizing LLM performance for specific tasks like classification or chat
Fine-tuning large models (7B+) on consumer-grade GPUs using QLoRA