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LLaMA-Factory project preview

LLaMA-Factory

Unified and efficient fine-tuning framework for 100+ LLMs and VLMs with a web UI.

LicenseApache-2.0
GitHub stars35.5k
Last commit1 weeks ago
Tags5 topics
Llm FinetuningGradio WebuiQlora TuningDeveloper ToolsDpo Alignment
Overview

Why consider LLaMA-Factory?

LLaMA-Factory is an open-source, web-based large language model fine-tuning suite written in Python by Yaowei Zheng. Supporting over 100 open foundation models (LLaMA 3, Mistral, Qwen, Gemma), it integrates LoRA, QLoRA, GaLore, and DPO alignment via an easy-to-use Gradio dashboard.

Guided learning

Learn LLaMA-Factory by building

Practical setup notes, real use cases, and copy-ready examples in one focused guide.

1 min read 2 sections
In this guide2 sections

Overview of LLaMA-Factory

LLaMA-Factory accelerates Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) via an interactive GUI.

Quickstart

bash
1git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
2cd LLaMA-Factory && pip install -e ".[torch,metrics]"
3llamafactory-cli webui

LLaMA-Factory is licensed under the Apache License Version 2.0.

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LLaMA-Factory FAQs

LLaMA-Factory is listed as a Developer Tools tool on TiloBox. Review the overview, features, and official documentation on this page to decide whether it solves your specific workflow.

Start with the project's GitHub repository and official website for supported installation and deployment instructions. Test the setup with representative data or a small project before rolling it out more widely.

LLaMA-Factory is listed under the Apache-2.0 license. Read the complete license text and the project's notices before using, modifying, or distributing the software.

Production readiness depends on your requirements. Review maintenance activity, security practices, documentation, backup and upgrade procedures, and compatibility with your stack; then validate it in a non-production environment.

LLaMA-Factory is listed as an alternative to Unsloth. Compare the core workflow, deployment model, integrations, and licensing against your must-have requirements before switching.