Is it suitable for beginners?

Yes, it includes guidance for both the user-friendly no-code WebUI interface and advanced script-based training for experienced researchers.

What models does this skill support?

It supports over 100 models including popular architectures like Llama, Qwen, Gemma, Mistral, and many others available on HuggingFace.

Can I use this for low-memory fine-tuning?

Yes, the skill provides specific guidance for memory-efficient training using 2-bit to 8-bit QLoRA techniques to fit large models on consumer hardware.

Does it support multimodal models?

Absolutely, it includes documentation and patterns for fine-tuning models that handle both text and visual data.

How do I trigger this skill in Claude Code?

This skill is automatically triggered when you ask about LLaMA-Factory features, implementation, debugging, or general LLM fine-tuning workflows.

LLaMA Factory Fine-Tuning

Name: LLaMA Factory Fine-Tuning
Author: zechenzhangAGI

byzechenzhangAGI

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384

Data Science & ML

Streamlines the fine-tuning process for over 100 large language models using the LLaMA-Factory framework and QLoRA techniques.

About

This skill provides comprehensive assistance for fine-tuning large language models (LLMs) via the LLaMA-Factory toolkit, offering expert guidance on no-code WebUI setups and advanced command-line implementations. It supports a vast library of 100+ models and efficient training methods like QLoRA and multimodal support, enabling developers to build specialized AI agents, optimize model performance, and troubleshoot complex training pipelines directly within their Claude Code workflow.

Key Features

Support for efficient fine-tuning techniques like 2/3/4/5/6/8-bit QLoRA and LoRA
Comprehensive debugging and best practices for LLM optimization and deployment
Expert guidance for 100+ supported LLMs including Llama, Qwen, and Gemma
Guidance on multimodal model training and complex dataset preparation
Step-by-step assistance for both WebUI no-code and API-based configurations
384 GitHub stars

Use Cases

Fine-tuning a Llama-3 model for specialized domain knowledge using QLoRA
Setting up a multimodal training pipeline for vision-language models
Troubleshooting hyperparameter settings and performance bottlenecks in LLaMA-Factory

About

Key Features

Support for efficient fine-tuning techniques like 2/3/4/5/6/8-bit QLoRA and LoRA
Comprehensive debugging and best practices for LLM optimization and deployment
Expert guidance for 100+ supported LLMs including Llama, Qwen, and Gemma
Guidance on multimodal model training and complex dataset preparation
Step-by-step assistance for both WebUI no-code and API-based configurations
384 GitHub stars

Use Cases

Fine-tuning a Llama-3 model for specialized domain knowledge using QLoRA
Setting up a multimodal training pipeline for vision-language models
Troubleshooting hyperparameter settings and performance bottlenecks in LLaMA-Factory