Project Title

LLaMA-Factory — Unified Efficient Fine-Tuning for 100+ Large Language Models

Overview

LLaMA-Factory is an open-source Python project designed to streamline the fine-tuning process for over 100 large language models (LLMs) and vision language models (VLMs). It offers a unified approach to model fine-tuning, making it accessible and efficient for developers and researchers. The project stands out for its zero-code CLI and Web UI, enabling easy fine-tuning with minimal setup.

Key Features

Unified fine-tuning framework for multiple LLMs and VLMs
Zero-code CLI and Web UI for easy model fine-tuning
Supports a wide range of models, including LLaMA, LLaMA3, and others
Integration with various platforms like Amazon SageMaker, NVIDIA, and Aliyun

Use Cases

Researchers and developers needing to fine-tune large language models for specific tasks without extensive coding.
Enterprises looking to enhance their NLP capabilities by customizing pre-trained models to their needs.
Educational institutions utilizing LLaMA-Factory for teaching and research purposes in natural language processing.

Advantages

Simplifies the fine-tuning process with a user-friendly interface
Broad compatibility with various models and platforms
Active community and regular updates ensure ongoing support and improvements

Limitations / Considerations

The project may require significant computational resources for fine-tuning large models.
Users should be aware of the licensing implications when using pre-trained models for commercial purposes.

Hugging Face Transformers: A library of pre-trained models for NLP, differing in its focus on providing a wide range of models rather than a unified fine-tuning framework.
TensorFlow Model Optimization Toolkit: Offers model optimization techniques, but is more focused on model compression and optimization rather than fine-tuning.
PyTorch Lightning: A framework for scaling PyTorch models, which can be used for fine-tuning but does not offer the same level of specialization as LLaMA-Factory.

Basic Information

GitHub: https://github.com/hiyouga/LLaMA-Factory
Stars: 57,457
License: Unknown
Last Commit: 2025-09-04

📊 Project Information

Project Name: LLaMA-Factory
GitHub URL: https://github.com/hiyouga/LLaMA-Factory
Programming Language: Python
⭐ Stars: 57,457
🍴 Forks: 7,040
📅 Created: 2023-05-28
🔄 Last Updated: 2025-09-04

🏷️ Project Topics

Topics: [, ", a, g, e, n, t, ", ,, , ", a, i, ", ,, , ", d, e, e, p, s, e, e, k, ", ,, , ", f, i, n, e, -, t, u, n, i, n, g, ", ,, , ", g, e, m, m, a, ", ,, , ", g, p, t, ", ,, , ", i, n, s, t, r, u, c, t, i, o, n, -, t, u, n, i, n, g, ", ,, , ", l, a, r, g, e, -, l, a, n, g, u, a, g, e, -, m, o, d, e, l, s, ", ,, , ", l, l, a, m, a, ", ,, , ", l, l, a, m, a, 3, ", ,, , ", l, l, m, ", ,, , ", l, o, r, a, ", ,, , ", m, o, e, ", ,, , ", n, l, p, ", ,, , ", p, e, f, t, ", ,, , ", q, l, o, r, a, ", ,, , ", q, u, a, n, t, i, z, a, t, i, o, n, ", ,, , ", q, w, e, n, ", ,, , ", r, l, h, f, ", ,, , ", t, r, a, n, s, f, o, r, m, e, r, s, ", ]