# <div align="center"><strong>LLaMA Factory</strong></div>
## 简介

LLaMA Factory是一个大语言模型训练和推理的框架，支持了魔搭社区（ModelScope）的模型和数据集资源。它允许用户通过内置的Web UI灵活定制100多个LLMs的微调，而无需编写代码。

## 项目特色

- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
- **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。
- **多种精度**：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。
- **先进算法**：GaLore、BAdam、Adam-mini、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
- **实用技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow 等等。
- **极速推理**：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。

## 支持模型结构列表


| 模型名                                                             | 参数量                            | Template            |
| ----------------------------------------------------------------- | -------------------------------- | ------------------- |
| [Baichuan 2](https://huggingface.co/baichuan-inc)                 | 7B/13B                           | baichuan2           |
| [ChatGLM3](https://huggingface.co/THUDM)                          | 6B                               | chatglm3            |
| [Gemma 2](https://huggingface.co/google)                          | 2B/9B                            | gemma               |
| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM)           | 9B/32B                           | glm4                |
| [Llama 2](https://huggingface.co/meta-llama)                      | 7B/13B/70B                       | llama2              |
| [Llama 3/Llama 3.1](https://huggingface.co/meta-llama)            | 8B/70B                           | llama3              |
| [Llama 4](https://huggingface.co/meta-llama)                      | 109B/402B                        | llama4              |
| [OLMo](https://hf-mirror.com/allenai)                             | 1B/7B                            | olmo                |
| [Qwen (1-2.5) (Code/Math/MoE/QwQ)](https://huggingface.co/Qwen)   | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen                |
| [Qwen3 (MoE)](https://huggingface.co/Qwen)                        | 0.6B/1.7B/4B/8B/14B/30B/32B/235B     | qwen3               |
| [XVERSE](https://hf-mirror.com/xverse)                            | 7B/13B                           | xverse              |

持续更新中...

> **[!NOTE]**
>
> 注意：本版本仅支持deepseek蒸馏模型的监督微调(SFT)，可参考[deepseek-r1-distill_vllm](https://developer.sourcefind.cn/codes/modelzoo/deepseek-r1-distill_vllm)
>
> 对于所有“基座”（Base）模型，`template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Instruct/Chat）模型请务必使用**对应的模板**。
>
> 请务必在训练和推理时采用**完全一致**的模板。
> 您也可以在 [template.py](src/llamafactory/data/template.py) 中添加自己的对话模板。
>
> **已知问题及解决方案**
> 1. `Baichuan 2` 需要卸载掉环境中的xformers库，当前仅支持Lora方式训练。
>
> 2. `XVERSE`在`tokenizer > 0.19`的版本下有兼容性问题报错`Exception: data did not match any variant of untagged enum PyPreTokenizerTypeWrappe`，需要使用[XVERSE-13B-256K-hf](https://huggingface.co/xverse/XVERSE-13B-256K/tree/main)中的`tokenizer_config.json.update`/`tokenizer.json.update`替换原有模型文件中的对应tokenizer文件，具体解决方法参考[xverse-ai/XVERSE-7B issues](https://github.com/xverse-ai/XVERSE-7B/issues/1)
>
> 3. `Qwen2`训练仅支持bf16格式，**fp16会出现loss为0，lr为0的问题**，参考[issues](https://github.com/hiyouga/LLaMA-Factory/issues/4848)
>
> 4. `deepspeed-cpu-offload-stage3`出现`RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!`错误，是deepspeed本身bug，解决办法参考官方[issuse](https://github.com/microsoft/DeepSpeed/issues/5634)

## 使用源码编译方式安装
### 环境准备

`-v 路径`、`docker_name`和`imageID`根据实际情况修改

####  Docker（方法一）

基于光源pytorch2.4.1基础镜像环境：镜像下载地址：[https://sourcefind.cn/#/image/dcu/pytorch](https://sourcefind.cn/#/image/dcu/pytorch)，根据pytorch2.4.1、python、dtk及系统下载对应的镜像版本。

```bash
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash

cd /your_code_path/llama_factory
pip install -e ".[torch,metrics]"
## llama4 需要单独安装以下包
pip install git+https://github.com/hiyouga/transformers.git@llama4_train
```

#### Dockerfile（方法二）

```bash
cd docker
docker build --no-cache -t llama-factory:latest .
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash

cd /your_code_path/llama_factory
pip install -e ".[torch,metrics]"
## llama4 需要单独安装以下包
pip install git+https://github.com/hiyouga/transformers.git@llama4_train
```

#### Anaconda（方法三）

关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
```bash
DTK: 25.04
python: 3.10
torch: 2.4.1
vllm: ≥0.4.3
deepspeed: 0.14.2+das.opt2.dtk2504
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`

### 源码编译安装

> [!TIP]
> 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。

```bash
git clone http://developer.hpccube.com/codes/OpenDAS/llama-factory.git
cd /your_code_path/llama_factory
pip install -e ".[torch,metrics]"
## llama4 需要单独安装以下包
pip install git+https://github.com/hiyouga/transformers.git@llama4_train

# （可选）deepspeed多机训练
# pdsh安装，若已安装，可忽略。
# 安装需要root权限
cd ../
#下载解压
wget https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/pdsh/pdsh-2.29.tar.bz2 && tar -xf pdsh-2.29.tar.bz2
#编译安装
cd pdsh-2.29 && ./configure --with-ssh --enable-static-modules --prefix=/usr/local && make && make install
#测试
pdsh -V
```

## 数据集

<details><summary>预训练数据集</summary>

- [Wiki Demo (en)](data/wiki_demo.txt)
- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)
- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220)
- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)
- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
- [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
- [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)

</details>

<details><summary>指令微调数据集</summary>

- [Identity (en&zh)](data/identity.json)
- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3)
- [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
- [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
- [UltraChat (en)](https://github.com/thunlp/UltraChat)
- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
- [SlimOrca (en)](https://huggingface.co/datasets/Open-Orca/SlimOrca)
- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
- [Wiki QA (en)](https://huggingface.co/datasets/wiki_qa)
- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
- [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
- [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
- [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
- [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction)
- [Ruozhiba (zh)](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo)
- [Neo-sft (zh)](https://huggingface.co/datasets/m-a-p/neo_sft_phase2)
- [Magpie-Pro-300K-Filtered (en)](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered)
- [Magpie-ultra-v0.1 (en)](https://huggingface.co/datasets/argilla/magpie-ultra-v0.1)
- [WebInstructSub (en)](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub)
- [OpenO1-SFT (en&zh)](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)
- [Open-Thoughts (en)](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k)
- [Open-R1-Math (en)](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k)
- [Chinese-DeepSeek-R1-Distill (zh)](https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT)
- [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
- [Pokemon-gpt4o-captions (en&zh)](https://huggingface.co/datasets/jugg1024/pokemon-gpt4o-captions)
- [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)
- [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)
- [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de)
- [OpenSchnabeltier (de)](https://huggingface.co/datasets/mayflowergmbh/openschnabeltier_de)
- [Evol Instruct (de)](https://huggingface.co/datasets/mayflowergmbh/evol-instruct_de)
- [Dolphin (de)](https://huggingface.co/datasets/mayflowergmbh/dolphin_de)
- [Booksum (de)](https://huggingface.co/datasets/mayflowergmbh/booksum_de)
- [Airoboros (de)](https://huggingface.co/datasets/mayflowergmbh/airoboros-3.0_de)
- [Ultrachat (de)](https://huggingface.co/datasets/mayflowergmbh/ultra-chat_de)

</details>

<details><summary>偏好数据集</summary>

- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
- [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
- [COIG-P (en&zh)](https://huggingface.co/datasets/m-a-p/COIG-P)
- [RLHF-V (en)](https://huggingface.co/datasets/openbmb/RLHF-V-Dataset)
- [VLFeedback (en)](https://huggingface.co/datasets/Zhihui/VLFeedback)
- [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
- [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
- [KTO mixed (en)](https://huggingface.co/datasets/argilla/kto-mix-15k)

</details>


部分数据集的使用需要确认，我们推荐使用下述命令登录您的 Hugging Face 账户。

```bash
pip install --upgrade huggingface_hub
huggingface-cli login
```

### 数据准备

关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace / ModelScope 上的数据集或加载本地数据集。

> [!NOTE]
> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。

## 如何使用
### 快速开始

下面三行命令分别对 Llama3-8B-Instruct 模型进行 LoRA **微调**、**推理**和**合并**。根据实际情况修改参数，如`model_name_or_path`/`dataset`/`template`等。

```bash
llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
```

高级用法请参考 [examples/README_zh.md](examples/README_zh.md)（包括多 GPU 微调）。

> [!TIP]
> 使用 `llamafactory-cli help` 显示帮助信息。
>
> 自有数据集推理精度验证方法推荐使用：`python scripts/vllm_infer.py`生成结果，`python scripts/eval_bleu_rouge.py`计算得分，具体参数信息请参考脚本内容。

## 参考资料

- [README_zh](README_zh.md)
- [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
