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README.md
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README.md
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[](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)
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[](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)
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[](https://pypi.org/project/llamafactory/)
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[](https://scholar.google.com/scholar?cites=12620864006390196564)
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[](https://scholar.google.com/scholar?cites=12620864006390196564)
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[](https://github.com/hiyouga/LLaMA-Factory/pulls)
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[](https://twitter.com/llamafactory_ai)
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@ -16,7 +16,9 @@
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[](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
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[](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
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[](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
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[](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)
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[](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/)
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### Used by [Amazon](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/), [NVIDIA](https://developer.nvidia.com/rtx/ai-toolkit), [Aliyun](https://help.aliyun.com/zh/pai/use-cases/fine-tune-a-llama-3-model-with-llama-factory), etc.
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<div align="center" markdown="1">
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@ -30,19 +32,13 @@
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[Available for MacOS, Linux, & Windows](https://warp.dev/llama-factory)
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</div>
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----
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<h3 align="center">
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Easily fine-tune 100+ large language models with zero-code <a href="#quickstart">CLI</a> and <a href="#fine-tuning-with-llama-board-gui-powered-by-gradio">Web UI</a>
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</h3>
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### Easily fine-tune 100+ large language models with zero-code [CLI](#quickstart) and [Web UI](#fine-tuning-with-llama-board-gui-powered-by-gradio)
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<p align="center">
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<picture>
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<img alt="Github trend" src="https://trendshift.io/api/badge/repositories/4535">
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</picture>
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</p>
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</div>
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👋 Join our [WeChat](assets/wechat.jpg) or [NPU user group](assets/wechat_npu.jpg).
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@ -58,7 +54,7 @@ Choose your path:
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- **Colab (free)**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
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- **Local machine**: Please refer to [usage](#getting-started)
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- **PAI-DSW (free trial)**: [Llama3 Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) | [Qwen2-VL Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl) | [DeepSeek-R1-Distill Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b)
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- **Amazon SageMaker**: [Blog](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)
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- **Amazon SageMaker**: [Blog](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/)
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- **Easy Dataset**: [Fine-tune on Synthetic Data](https://buaa-act.feishu.cn/wiki/GVzlwYcRFiR8OLkHbL6cQpYin7g)
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> [!NOTE]
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@ -67,7 +63,7 @@ Choose your path:
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## Table of Contents
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- [Features](#features)
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- [Benchmark](#benchmark)
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- [Blogs](#blogs)
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- [Changelog](#changelog)
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- [Supported Models](#supported-models)
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- [Supported Training Approaches](#supported-training-approaches)
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@ -107,18 +103,17 @@ Choose your path:
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| Day 0 | Qwen3 / Qwen2.5-VL / Gemma 3 / InternLM 3 / MiniCPM-o-2.6 |
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| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4 |
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## Benchmark
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## Blogs
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Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning), LLaMA Factory's LoRA tuning offers up to **3.7 times faster** training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.
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- [How Apoidea Group enhances visual information extraction from banking documents with multimodal models using LLaMA-Factory on Amazon SageMaker HyperPod](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/) (English)
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- [Easy Dataset × LLaMA Factory: Enabling LLMs to Efficiently Learn Domain Knowledge](https://buaa-act.feishu.cn/wiki/GVzlwYcRFiR8OLkHbL6cQpYin7g) (English)
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- [LLaMA Factory: Fine-tuning the DeepSeek-R1-Distill-Qwen-7B Model For News Classifier](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b) (Chinese)
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<details><summary>All Blogs</summary>
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<details><summary>Definitions</summary>
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- **Training Speed**: the number of training samples processed per second during the training. (bs=4, cutoff_len=1024)
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- **Rouge Score**: Rouge-2 score on the development set of the [advertising text generation](https://aclanthology.org/D19-1321.pdf) task. (bs=4, cutoff_len=1024)
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- **GPU Memory**: Peak GPU memory usage in 4-bit quantized training. (bs=1, cutoff_len=1024)
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- We adopt `pre_seq_len=128` for ChatGLM's P-Tuning and `lora_rank=32` for LLaMA Factory's LoRA tuning.
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- [A One-Stop Code-Free Model Fine-Tuning \& Deployment Platform based on SageMaker and LLaMA-Factory](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/) (Chinese)
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- [LLaMA Factory Multi-Modal Fine-Tuning Practice: Fine-Tuning Qwen2-VL for Personal Tourist Guide](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl) (Chinese)
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- [LLaMA Factory: Fine-tuning the LLaMA3 Model for Role-Playing](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) (Chinese)
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</details>
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README_zh.md
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README_zh.md
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[](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)
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[](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)
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[](https://pypi.org/project/llamafactory/)
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[](https://scholar.google.com/scholar?cites=12620864006390196564)
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[](https://scholar.google.com/scholar?cites=12620864006390196564)
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[](https://github.com/hiyouga/LLaMA-Factory/pulls)
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[](https://twitter.com/llamafactory_ai)
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@ -18,16 +18,27 @@
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[](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
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[](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)
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<h3 align="center">
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使用零代码<a href="#快速开始">命令行</a>与 <a href="#llama-board-可视化微调由-gradio-驱动">Web UI</a> 轻松微调百余种大模型
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</h3>
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### 获得[亚马逊](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)、[英伟达](https://developer.nvidia.cn/rtx/ai-toolkit)、[阿里云](https://help.aliyun.com/zh/pai/use-cases/fine-tune-a-llama-3-model-with-llama-factory)等的应用。
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<p align="center">
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<picture>
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<img alt="Github trend" src="https://trendshift.io/api/badge/repositories/4535">
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</picture>
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</p>
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<div align="center" markdown="1">
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### 赞助商 ❤️
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<a href="https://warp.dev/llama-factory">
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<img alt="Warp sponsorship" width="400" src="https://github.com/user-attachments/assets/ab8dd143-b0fd-4904-bdc5-dd7ecac94eae">
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</a>
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#### [Warp，面向开发者的智能终端](https://warp.dev/llama-factory)
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[适用于 MacOS、Linux 和 Windows](https://warp.dev/llama-factory)
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----
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### 使用零代码[命令行](#快速开始)与 [Web UI](#llama-board-可视化微调由-gradio-驱动) 轻松微调百余种大模型
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</div>
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👋 加入我们的[微信群](assets/wechat.jpg)或 [NPU 用户群](assets/wechat_npu.jpg)。
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## 目录
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- [项目特色](#项目特色)
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- [性能指标](#性能指标)
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- [官方博客](#官方博客)
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- [更新日志](#更新日志)
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- [模型](#模型)
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- [训练方法](#训练方法)
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| Day 0 | Qwen3 / Qwen2.5-VL / Gemma 3 / InternLM 3 / MiniCPM-o-2.6 |
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| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4 |
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## 性能指标
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## 官方博客
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与 ChatGLM 官方的 [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning) 微调相比，LLaMA Factory 的 LoRA 微调提供了 **3.7 倍**的加速比，同时在广告文案生成任务上取得了更高的 Rouge 分数。结合 4 比特量化技术，LLaMA Factory 的 QLoRA 微调进一步降低了 GPU 显存消耗。
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- [通过亚马逊 SageMaker HyperPod 上的 LLaMA-Factory 增强多模态模型银行文档的视觉信息提取](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/)（英文）
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- [Easy Dataset × LLaMA Factory: 让大模型高效学习领域知识](https://buaa-act.feishu.cn/wiki/KY9xwTGs1iqHrRkjXBwcZP9WnL9)（中文）
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- [LLaMA Factory：微调DeepSeek-R1-Distill-Qwen-7B模型实现新闻标题分类器](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b)（中文）
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<details><summary>全部博客</summary>
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<details><summary>变量定义</summary>
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- **Training Speed**: 训练阶段每秒处理的样本数量。（批处理大小=4，截断长度=1024）
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- **Rouge Score**: [广告文案生成](https://aclanthology.org/D19-1321.pdf)任务验证集上的 Rouge-2 分数。（批处理大小=4，截断长度=1024）
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- **GPU Memory**: 4 比特量化训练的 GPU 显存峰值。（批处理大小=1，截断长度=1024）
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- 我们在 ChatGLM 的 P-Tuning 中采用 `pre_seq_len=128`，在 LLaMA Factory 的 LoRA 微调中采用 `lora_rank=32`。
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- [基于 Amazon SageMaker 和 LLaMA-Factory 打造一站式无代码模型微调部署平台 Model Hub](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)（中文）
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- [LLaMA Factory多模态微调实践：微调Qwen2-VL构建文旅大模型](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)（中文）
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- [LLaMA Factory：微调LLaMA3模型实现角色扮演](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)（中文）
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</details>
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