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llama-factory

Provides guidance for fine-tuning LLMs with LLaMA-Factory via CLI or WebUI.

Updated Aug 23, 2026
Search Tags:#llm#fine-tuning#webui#huggingface#qlora#llama-factory
One-click install
npx skills add https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026 --skill llama-factory-doanngoccuong
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-factory
Source: https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026/tree/main/.claude/skills/llama-factory
Command: npx skills add https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026 --skill llama-factory-doanngoccuong

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires llmtuner, torch, transformers, datasets, peft, accelerate, gradio, and includes references (resource) components.

What problem does it solve?

This Skill provides expert guidance and comprehensive documentation for fine-tuning Large Language Models (LLMs) using the LLaMA-Factory framework, enabling users to customize and optimize models without extensive coding.

Core Features & Use Cases

  • LLaMA-Factory Expertise: Offers in-depth knowledge on installation, training, evaluation, merging, and inference with LLaMA-Factory.
  • Model Fine-Tuning: Supports a wide range of models and fine-tuning techniques including QLoRA, LoRA, and full parameter tuning.
  • Use Case: A user wants to fine-tune the Qwen2.5-1.5B model for a specific sentiment analysis task. They can use this skill to understand the data preparation, training configuration, and evaluation steps required within LLaMA-Factory.

Quick Start

Use the llama-factory skill to get started with installing LLaMA-Factory on Linux.

Frequently Asked Questions about llama-factory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a large language model without writing extensive code?▼

You can fine-tune large language models without extensive code by using the LLaMA-Factory WebUI, which facilitates no-code LLM customization through a graphical interface for data preparation, training configuration, and evaluation.

What is the difference between QLoRA, LoRA, and full parameter fine-tuning?▼

QLoRA, LoRA, and full parameter fine-tuning are distinct techniques supported by LLaMA-Factory for LLM customization, each offering different trade-offs between memory efficiency, training speed, and model performance.

How do I install LLaMA-Factory and its dependencies on Linux?▼

To install LLaMA-Factory on Linux, you need to set up the environment with dependencies including torch, transformers, datasets, peft, and accelerate before configuring the training framework.

Can I use Huggingface models for training with this fine-tuning framework?▼

Yes, Huggingface models are supported for training, allowing you to load various pre-trained models and apply fine-tuning techniques like QLoRA within the framework.

Does LLaMA-Factory support distributed training and NPU acceleration?▼

Yes, LLaMA-Factory provides expert guidance on utilizing distributed training and NPU acceleration to optimize the fine-tuning process for large language models.

How do I evaluate and merge models after fine-tuning?▼

After fine-tuning, the framework provides documentation on evaluation steps and merging processes to integrate your trained weights back into the base model for inference.

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