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README.md
69
README.md
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@ -276,18 +276,19 @@ huggingface-cli login
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| ------------ | ------- | --------- |
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| python | 3.8 | 3.10 |
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| torch | 1.13.1 | 2.2.0 |
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| transformers | 4.37.2 | 4.39.3 |
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| datasets | 2.14.3 | 2.18.0 |
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| accelerate | 0.27.2 | 0.28.0 |
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| transformers | 4.37.2 | 4.40.1 |
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| datasets | 2.14.3 | 2.19.1 |
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| accelerate | 0.27.2 | 0.30.0 |
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| peft | 0.9.0 | 0.10.0 |
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| trl | 0.8.1 | 0.8.1 |
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| trl | 0.8.1 | 0.8.6 |
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| Optional | Minimum | Recommend |
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| ------------ | ------- | --------- |
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| CUDA | 11.6 | 12.2 |
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| deepspeed | 0.10.0 | 0.14.0 |
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| bitsandbytes | 0.39.0 | 0.43.0 |
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| flash-attn | 2.3.0 | 2.5.6 |
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| bitsandbytes | 0.39.0 | 0.43.1 |
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| vllm | 0.4.0 | 0.4.2 |
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| flash-attn | 2.3.0 | 2.5.8 |
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### Hardware Requirement
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@ -305,24 +306,15 @@ huggingface-cli login
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## Getting Started
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### Data Preparation
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Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope hub or load the dataset in local disk.
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> [!NOTE]
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> Please update `data/dataset_info.json` to use your custom dataset.
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### Dependence Installation
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### Installation
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```bash
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git clone https://github.com/hiyouga/LLaMA-Factory.git
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conda create -n llama_factory python=3.10
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conda activate llama_factory
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cd LLaMA-Factory
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pip install -e .[metrics]
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```
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Extra dependencies available: deepspeed, metrics, galore, badam, vllm, bitsandbytes, gptq, awq, aqlm, qwen, modelscope, quality
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Extra dependencies available: metrics, deepspeed, bitsandbytes, vllm, galore, badam, gptq, awq, aqlm, qwen, modelscope, quality
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<details><summary>For Windows users</summary>
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@ -336,19 +328,41 @@ To enable FlashAttention-2 on the Windows platform, you need to install the prec
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</details>
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### Train with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))
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### Data Preparation
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Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope hub or load the dataset in local disk.
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> [!NOTE]
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> Please update `data/dataset_info.json` to use your custom dataset.
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### Quickstart
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The following 3 commands conduct LoRA fine-tuning, inference and merging for Llama3-8B-Instruct model, respectively.
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
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```
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See [examples/README.md](examples/README.md) for advanced usage.
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> [!TIP]
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> Use `llamafactory-cli help` to show help information.
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### Use LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))
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> [!IMPORTANT]
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> LLaMA Board GUI only supports training on a single GPU, please use [CLI](#train-with-command-line-interface) for distributed training.
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> LLaMA Board GUI only supports training on a single GPU.
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#### Use local environment
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```bash
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llamafactory-cli webui
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli webui
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```
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> [!TIP]
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> To modify the default setting in the LLaMA Board GUI, you can use environment variables, e.g., `export CUDA_VISIBLE_DEVICES=0 GRADIO_SERVER_NAME=0.0.0.0 GRADIO_SERVER_PORT=7860 GRADIO_SHARE=False` (use `set` command on Windows OS).
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> To modify the default setting in the LLaMA Board GUI, you can use environment variables, e.g., `export GRADIO_SERVER_NAME=0.0.0.0 GRADIO_SERVER_PORT=7860 GRADIO_SHARE=False` (use `set` command on Windows OS).
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<details><summary>For Alibaba Cloud users</summary>
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@ -389,21 +403,10 @@ docker compose -f ./docker-compose.yml up -d
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</details>
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### Train with Command Line Interface
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See [examples/README.md](examples/README.md) for usage.
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> [!TIP]
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> Use `llamafactory-cli train -h` to display arguments description.
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### Deploy with OpenAI-style API and vLLM
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```bash
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CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api \
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--model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct \
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--template llama3 \
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--infer_backend vllm \
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--vllm_enforce_eager
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CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
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```
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### Download from ModelScope Hub
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71
README_zh.md
71
README_zh.md
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@ -163,7 +163,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
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| [Yuan](https://huggingface.co/IEITYuan) | 2B/51B/102B | q_proj,v_proj | yuan |
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> [!NOTE]
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> **默认模块**应作为 `--lora_target` 参数的默认值，可使用 `--lora_target all` 参数指定全部模块以得到更好的效果。
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> **默认模块**应作为 `--lora_target` 参数的默认值，可使用 `--lora_target all` 参数指定全部模块以取得更好的效果。
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>
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> 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**。
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>
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@ -276,18 +276,19 @@ huggingface-cli login
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| ------------ | ------- | --------- |
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| python | 3.8 | 3.10 |
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| torch | 1.13.1 | 2.2.0 |
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| transformers | 4.37.2 | 4.39.3 |
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| datasets | 2.14.3 | 2.18.0 |
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| accelerate | 0.27.2 | 0.28.0 |
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| transformers | 4.37.2 | 4.40.1 |
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| datasets | 2.14.3 | 2.19.1 |
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| accelerate | 0.27.2 | 0.30.0 |
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| peft | 0.9.0 | 0.10.0 |
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| trl | 0.8.1 | 0.8.1 |
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| trl | 0.8.1 | 0.8.6 |
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| 可选项 | 至少 | 推荐 |
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| ------------ | ------- | --------- |
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| CUDA | 11.6 | 12.2 |
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| deepspeed | 0.10.0 | 0.14.0 |
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| bitsandbytes | 0.39.0 | 0.43.0 |
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| flash-attn | 2.3.0 | 2.5.6 |
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| bitsandbytes | 0.39.0 | 0.43.1 |
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| vllm | 0.4.0 | 0.4.2 |
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| flash-attn | 2.3.0 | 2.5.8 |
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### 硬件依赖
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@ -305,24 +306,15 @@ huggingface-cli login
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## 如何使用
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### 数据准备
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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。
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> [!NOTE]
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> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。
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### 安装依赖
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### 安装 LLaMA Factory
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```bash
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git clone https://github.com/hiyouga/LLaMA-Factory.git
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conda create -n llama_factory python=3.10
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conda activate llama_factory
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cd LLaMA-Factory
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pip install -e .[metrics]
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```
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可选的额外依赖项：deepspeed、metrics、galore、badam、vllm、bitsandbytes、gptq、awq、aqlm、qwen、modelscope、quality
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可选的额外依赖项：metrics、deepspeed、bitsandbytes、vllm、galore、badam、gptq、awq、aqlm、qwen、modelscope、quality
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<details><summary>Windows 用户指南</summary>
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@ -336,19 +328,41 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
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</details>
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### 利用 LLaMA Board 可视化界面训练（由 [Gradio](https://github.com/gradio-app/gradio) 驱动）
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### 数据准备
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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。
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> [!NOTE]
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> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。
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### 快速开始
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下面三行命令分别对 Llama3-8B-Instruct 模型进行 LoRA 微调、推理和合并。
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
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```
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高级用法请参考 [examples/README_zh.md](examples/README_zh.md)。
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> [!TIP]
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> 使用 `llamafactory-cli help` 显示使用帮助。
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### 使用 LLaMA Board 可视化界面（由 [Gradio](https://github.com/gradio-app/gradio) 驱动）
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> [!IMPORTANT]
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> LLaMA Board 可视化界面目前仅支持单 GPU 训练，请使用[命令行接口](#利用命令行接口训练)来进行多 GPU 分布式训练。
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> LLaMA Board 可视化界面目前仅支持单 GPU 训练。
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#### 使用本地环境
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```bash
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llamafactory-cli webui
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli webui
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```
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> [!TIP]
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> 您可以使用环境变量来修改 LLaMA Board 可视化界面的默认设置，例如 `export CUDA_VISIBLE_DEVICES=0 GRADIO_SERVER_NAME=0.0.0.0 GRADIO_SERVER_PORT=7860 GRADIO_SHARE=False`（Windows 系统可使用 `set` 指令）。
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> 您可以使用环境变量来修改 LLaMA Board 可视化界面的默认设置，例如 `export GRADIO_SERVER_NAME=0.0.0.0 GRADIO_SERVER_PORT=7860 GRADIO_SHARE=False`（Windows 系统可使用 `set` 指令）。
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<details><summary>阿里云用户指南</summary>
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@ -389,21 +403,10 @@ docker compose -f ./docker-compose.yml up -d
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</details>
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### 利用命令行接口训练
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使用方法请参考 [examples/README_zh.md](examples/README_zh.md)。
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> [!TIP]
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> 您可以执行 `llamafactory-cli train -h` 来查看参数文档。
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### 利用 vLLM 部署 OpenAI API
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```bash
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CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api \
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--model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct \
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--template llama3 \
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--infer_backend vllm \
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--vllm_enforce_eager
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CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
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```
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### 从魔搭社区下载
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We provide diverse examples about fine-tuning LLMs.
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```bash
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export CUDA_VISIBLE_DEVICES=0
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cd examples/lora_single_gpu
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llamafactory-cli train llama3_lora_pretrain.yaml # Do continuous pre-training using LoRA
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```
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```
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examples/
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├── lora_single_gpu/
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│ ├── pretrain.sh: Do continuous pre-training using LoRA
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│ ├── `
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│ ├── sft.sh: Do supervised fine-tuning using LoRA
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│ ├── reward.sh: Do reward modeling using LoRA
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│ ├── ppo.sh: Do PPO training using LoRA
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@ -10,7 +10,7 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
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--finetuning_type full \
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--use_badam \
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--badam_switch_mode descending \
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--badam_switch_interval 50 \
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--badam_switch_block_every 50 \
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--badam_verbose 2 \
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--output_dir ../../../saves/LLaMA2-7B/badam/sft \
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--overwrite_cache \
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@ -1,7 +0,0 @@
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#!/bin/bash
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CUDA_VISIBLE_DEVICES=0 API_PORT=8000 llamafactory-cli api \
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--model_name_or_path meta-llama/Llama-2-7b-hf \
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--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
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--template default \
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--finetuning_type lora
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@ -1,7 +0,0 @@
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#!/bin/bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat \
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--model_name_or_path meta-llama/Llama-2-7b-hf \
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--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
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--template default \
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--finetuning_type lora
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@ -1,12 +0,0 @@
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#!/bin/bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli eval \
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--model_name_or_path meta-llama/Llama-2-7b-hf \
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--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
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--template fewshot \
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--finetuning_type lora \
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--task mmlu \
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--split test \
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--lang en \
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--n_shot 5 \
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--batch_size 4
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@ -0,0 +1,2 @@
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model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
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template: llama3
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@ -0,0 +1,4 @@
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model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
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adapter_name_or_path: saves/llama3-8b/lora/sft
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template: llama3
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finetuning_type: lora
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@ -0,0 +1,4 @@
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model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
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template: llama3
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infer_backend: vllm
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vllm_enforce_eager: true
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@ -1,8 +0,0 @@
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#!/bin/bash
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# add `--visual_inputs True` to load MLLM
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli webchat \
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--model_name_or_path meta-llama/Llama-2-7b-hf \
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--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
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--template default \
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--finetuning_type lora
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@ -1,35 +0,0 @@
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#!/bin/bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
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--stage dpo \
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--do_train \
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--model_name_or_path meta-llama/Llama-2-7b-hf \
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--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
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--create_new_adapter \
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--dataset orca_rlhf \
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--dataset_dir ../../data \
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--template default \
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--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--output_dir ../../saves/LLaMA2-7B/lora/dpo \
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--overwrite_cache \
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--overwrite_output_dir \
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--cutoff_len 1024 \
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--preprocessing_num_workers 16 \
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--per_device_train_batch_size 1 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 8 \
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--lr_scheduler_type cosine \
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--logging_steps 10 \
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--warmup_steps 20 \
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--save_steps 100 \
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--eval_steps 100 \
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--evaluation_strategy steps \
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--load_best_model_at_end \
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--learning_rate 1e-5 \
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--num_train_epochs 1.0 \
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--max_samples 1000 \
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--val_size 0.1 \
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--dpo_ftx 1.0 \
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--plot_loss \
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--fp16
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@ -0,0 +1,39 @@
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# model
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model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
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# method
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stage: dpo
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do_train: true
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finetuning_type: lora
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lora_target: q_proj,v_proj
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dpo_ftx: 1.0
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# dataset
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dataset: orca_rlhf
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template: llama3
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cutoff_len: 1024
|
||||
max_samples: 1000
|
||||
val_size: 0.1
|
||||
overwrite_cache: true
|
||||
preprocessing_num_workers: 16
|
||||
|
||||
# output
|
||||
output_dir: saves/llama3-8b/lora/dpo
|
||||
logging_steps: 10
|
||||
save_steps: 500
|
||||
plot_loss: true
|
||||
overwrite_output_dir: true
|
||||
|
||||
# train
|
||||
per_device_train_batch_size: 1
|
||||
gradient_accumulation_steps: 8
|
||||
learning_rate: 0.00001
|
||||
num_train_epochs: 3.0
|
||||
lr_scheduler_type: cosine
|
||||
warmup_steps: 0.1
|
||||
fp16: true
|
||||
|
||||
# eval
|
||||
per_device_eval_batch_size: 1
|
||||
evaluation_strategy: steps
|
||||
eval_steps: 500
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
# model
|
||||
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
|
||||
adapter_name_or_path: saves/llama3-8b/lora/sft
|
||||
|
||||
# method
|
||||
finetuning_type: lora
|
||||
|
||||
# dataset
|
||||
task: mmlu
|
||||
split: test
|
||||
template: fewshot
|
||||
lang: en
|
||||
n_shot: 5
|
||||
|
||||
# output
|
||||
save_dir: saves/llama3-8b/lora/eval
|
||||
|
||||
# eval
|
||||
batch_size: 4
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
# model
|
||||
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
|
||||
|
||||
# method
|
||||
stage: orpo
|
||||
do_train: true
|
||||
finetuning_type: lora
|
||||
lora_target: q_proj,v_proj
|
||||
|
||||
# dataset
|
||||
dataset: orca_rlhf
|
||||
template: llama3
|
||||
cutoff_len: 1024
|
||||
max_samples: 1000
|
||||
val_size: 0.1
|
||||
overwrite_cache: true
|
||||
preprocessing_num_workers: 16
|
||||
|
||||
# output
|
||||
output_dir: saves/llama3-8b/lora/orpo
|
||||
logging_steps: 10
|
||||
save_steps: 500
|
||||
plot_loss: true
|
||||
overwrite_output_dir: true
|
||||
|
||||
# train
|
||||
per_device_train_batch_size: 1
|
||||
gradient_accumulation_steps: 8
|
||||
learning_rate: 0.00001
|
||||
num_train_epochs: 3.0
|
||||
lr_scheduler_type: cosine
|
||||
warmup_steps: 0.1
|
||||
fp16: true
|
||||
|
||||
# eval
|
||||
per_device_eval_batch_size: 1
|
||||
evaluation_strategy: steps
|
||||
eval_steps: 500
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
# model
|
||||
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
|
||||
reward_model: saves/llama3-8b/lora/reward
|
||||
|
||||
# method
|
||||
stage: ppo
|
||||
do_train: true
|
||||
finetuning_type: lora
|
||||
lora_target: q_proj,v_proj
|
||||
|
||||
# dataset
|
||||
dataset: identity,alpaca_gpt4_en
|
||||
template: llama3
|
||||
cutoff_len: 1024
|
||||
max_samples: 1000
|
||||
overwrite_cache: true
|
||||
preprocessing_num_workers: 16
|
||||
|
||||
# output
|
||||
output_dir: saves/llama3-8b/lora/ppo
|
||||
logging_steps: 10
|
||||
save_steps: 500
|
||||
plot_loss: true
|
||||
overwrite_output_dir: true
|
||||
|
||||
# train
|
||||
per_device_train_batch_size: 1
|
||||
gradient_accumulation_steps: 8
|
||||
learning_rate: 0.00001
|
||||
num_train_epochs: 3.0
|
||||
lr_scheduler_type: cosine
|
||||
warmup_steps: 0.1
|
||||
fp16: true
|
||||
|
||||
# generate
|
||||
max_new_tokens: 512
|
||||
top_k: 0
|
||||
top_p: 0.9
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
# model
|
||||
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
|
||||
adapter_name_or_path: saves/llama3-8b/lora/sft
|
||||
|
||||
# method
|
||||
stage: sft
|
||||
do_predict: true
|
||||
finetuning_type: lora
|
||||
|
||||
# dataset
|
||||
dataset: identity,alpaca_gpt4_en
|
||||
template: llama3
|
||||
cutoff_len: 1024
|
||||
max_samples: 50
|
||||
overwrite_cache: true
|
||||
preprocessing_num_workers: 16
|
||||
|
||||
# output
|
||||
output_dir: saves/llama3-8b/lora/predict
|
||||
overwrite_output_dir: true
|
||||
|
||||
# eval
|
||||
per_device_eval_batch_size: 1
|
||||
predict_with_generate: true
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
# model
|
||||
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
|
||||
|
||||
# method
|
||||
stage: pt
|
||||
do_train: true
|
||||
finetuning_type: lora
|
||||
lora_target: q_proj,v_proj
|
||||
|
||||
# dataset
|
||||
dataset: c4_demo
|
||||
cutoff_len: 1024
|
||||
max_samples: 1000
|
||||
val_size: 0.1
|
||||
overwrite_cache: true
|
||||
preprocessing_num_workers: 16
|
||||
|
||||
# output
|
||||
output_dir: saves/llama3-8b/lora/sft
|
||||
logging_steps: 10
|
||||
save_steps: 500
|
||||
plot_loss: true
|
||||
overwrite_output_dir: true
|
||||
|
||||
# train
|
||||
per_device_train_batch_size: 1
|
||||
gradient_accumulation_steps: 8
|
||||
learning_rate: 0.0001
|
||||
num_train_epochs: 3.0
|
||||
lr_scheduler_type: cosine
|
||||
warmup_steps: 0.1
|
||||
fp16: true
|
||||
|
||||
# eval
|
||||
per_device_eval_batch_size: 1
|
||||
evaluation_strategy: steps
|
||||
eval_steps: 500
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
# model
|
||||
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
|
||||
|
||||
# method
|
||||
stage: rm
|
||||
do_train: true
|
||||
finetuning_type: lora
|
||||
lora_target: q_proj,v_proj
|
||||
|
||||
# dataset
|
||||
dataset: orca_rlhf
|
||||
template: llama3
|
||||
cutoff_len: 1024
|
||||
max_samples: 1000
|
||||
val_size: 0.1
|
||||
overwrite_cache: true
|
||||
preprocessing_num_workers: 16
|
||||
|
||||
# output
|
||||
output_dir: saves/llama3-8b/lora/reward
|
||||
logging_steps: 10
|
||||
save_steps: 500
|
||||
plot_loss: true
|
||||
overwrite_output_dir: true
|
||||
|
||||
# train
|
||||
per_device_train_batch_size: 1
|
||||
gradient_accumulation_steps: 8
|
||||
learning_rate: 0.00001
|
||||
num_train_epochs: 3.0
|
||||
lr_scheduler_type: cosine
|
||||
warmup_steps: 0.1
|
||||
fp16: true
|
||||
|
||||
# eval
|
||||
per_device_eval_batch_size: 1
|
||||
evaluation_strategy: steps
|
||||
eval_steps: 500
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
# model
|
||||
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
|
||||
|
||||
# method
|
||||
stage: sft
|
||||
do_train: true
|
||||
finetuning_type: lora
|
||||
lora_target: q_proj,v_proj
|
||||
|
||||
# dataset
|
||||
dataset: identity,alpaca_gpt4_en
|
||||
template: llama3
|
||||
cutoff_len: 1024
|
||||
max_samples: 1000
|
||||
val_size: 0.1
|
||||
overwrite_cache: true
|
||||
preprocessing_num_workers: 16
|
||||
|
||||
# output
|
||||
output_dir: saves/llama3-8b/lora/sft
|
||||
logging_steps: 10
|
||||
save_steps: 500
|
||||
plot_loss: true
|
||||
overwrite_output_dir: true
|
||||
|
||||
# train
|
||||
per_device_train_batch_size: 1
|
||||
gradient_accumulation_steps: 8
|
||||
learning_rate: 0.0001
|
||||
num_train_epochs: 3.0
|
||||
lr_scheduler_type: cosine
|
||||
warmup_steps: 0.1
|
||||
fp16: true
|
||||
|
||||
# eval
|
||||
per_device_eval_batch_size: 1
|
||||
evaluation_strategy: steps
|
||||
eval_steps: 500
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
# model
|
||||
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
|
||||
|
||||
# method
|
||||
stage: sft
|
||||
do_train: true
|
||||
finetuning_type: lora
|
||||
lora_target: q_proj,v_proj
|
||||
|
||||
# dataset
|
||||
dataset: identity,alpaca_gpt4_en
|
||||
template: llama3
|
||||
cutoff_len: 1024
|
||||
max_samples: 1000
|
||||
val_size: 0.1
|
||||
overwrite_cache: true
|
||||
preprocessing_num_workers: 16
|
||||
tokenized_path: saves/llama3-8b/dataset/sft # use `tokenized_path` in config to load data
|
||||
|
||||
# output
|
||||
output_dir: saves/llama3-8b/lora/sft
|
||||
overwrite_output_dir: true
|
||||
|
|
@ -0,0 +1,39 @@
|
|||
# model
|
||||
model_name_or_path: llava-hf/llava-1.5-7b-hf
|
||||
visual_inputs: true
|
||||
|
||||
# method
|
||||
stage: sft
|
||||
do_train: true
|
||||
finetuning_type: lora
|
||||
lora_target: q_proj,v_proj
|
||||
|
||||
# dataset
|
||||
dataset: mllm_demo
|
||||
template: vicuna
|
||||
cutoff_len: 1024
|
||||
max_samples: 1000
|
||||
val_size: 0.1
|
||||
overwrite_cache: true
|
||||
preprocessing_num_workers: 16
|
||||
|
||||
# output
|
||||
output_dir: saves/llava1_5-7b/lora/sft
|
||||
logging_steps: 10
|
||||
save_steps: 500
|
||||
plot_loss: true
|
||||
overwrite_output_dir: true
|
||||
|
||||
# train
|
||||
per_device_train_batch_size: 1
|
||||
gradient_accumulation_steps: 8
|
||||
learning_rate: 0.0001
|
||||
num_train_epochs: 3.0
|
||||
lr_scheduler_type: cosine
|
||||
warmup_steps: 0.1
|
||||
fp16: true
|
||||
|
||||
# eval
|
||||
per_device_eval_batch_size: 1
|
||||
evaluation_strategy: steps
|
||||
eval_steps: 500
|
||||
|
|
@ -1,32 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
|
||||
--stage orpo \
|
||||
--do_train \
|
||||
--model_name_or_path meta-llama/Llama-2-7b-hf \
|
||||
--dataset orca_rlhf \
|
||||
--dataset_dir ../../data \
|
||||
--template default \
|
||||
--finetuning_type lora \
|
||||
--lora_target q_proj,v_proj \
|
||||
--output_dir ../../saves/LLaMA2-7B/lora/orpo \
|
||||
--overwrite_cache \
|
||||
--overwrite_output_dir \
|
||||
--cutoff_len 1024 \
|
||||
--preprocessing_num_workers 16 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--gradient_accumulation_steps 8 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_steps 10 \
|
||||
--warmup_steps 20 \
|
||||
--save_steps 100 \
|
||||
--eval_steps 100 \
|
||||
--evaluation_strategy steps \
|
||||
--load_best_model_at_end \
|
||||
--learning_rate 1e-5 \
|
||||
--num_train_epochs 1.0 \
|
||||
--max_samples 1000 \
|
||||
--val_size 0.1 \
|
||||
--plot_loss \
|
||||
--fp16
|
||||
|
|
@ -1,32 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
|
||||
--stage ppo \
|
||||
--do_train \
|
||||
--model_name_or_path meta-llama/Llama-2-7b-hf \
|
||||
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
|
||||
--create_new_adapter \
|
||||
--dataset alpaca_gpt4_en \
|
||||
--dataset_dir ../../data \
|
||||
--template default \
|
||||
--finetuning_type lora \
|
||||
--lora_target q_proj,v_proj \
|
||||
--reward_model ../../saves/LLaMA2-7B/lora/reward \
|
||||
--output_dir ../../saves/LLaMA2-7B/lora/ppo \
|
||||
--overwrite_cache \
|
||||
--overwrite_output_dir \
|
||||
--cutoff_len 512 \
|
||||
--preprocessing_num_workers 16 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--gradient_accumulation_steps 8 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_steps 10 \
|
||||
--save_steps 100 \
|
||||
--learning_rate 1e-5 \
|
||||
--num_train_epochs 1.0 \
|
||||
--max_samples 1000 \
|
||||
--top_k 0 \
|
||||
--top_p 0.9 \
|
||||
--max_new_tokens 256 \
|
||||
--plot_loss \
|
||||
--fp16
|
||||
|
|
@ -1,19 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
|
||||
--stage sft \
|
||||
--do_predict \
|
||||
--model_name_or_path meta-llama/Llama-2-7b-hf \
|
||||
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft,../../saves/LLaMA2-7B/lora/dpo \
|
||||
--dataset alpaca_gpt4_en,glaive_toolcall \
|
||||
--dataset_dir ../../data \
|
||||
--template default \
|
||||
--finetuning_type lora \
|
||||
--output_dir ../../saves/LLaMA2-7B/lora/predict \
|
||||
--overwrite_cache \
|
||||
--overwrite_output_dir \
|
||||
--cutoff_len 1024 \
|
||||
--preprocessing_num_workers 16 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--max_samples 20 \
|
||||
--predict_with_generate
|
||||
|
|
@ -1,19 +0,0 @@
|
|||
#!/bin/bash
|
||||
# use `--tokenized_path` in training script to load data
|
||||
|
||||
CUDA_VISIBLE_DEVICES= llamafactory-cli train \
|
||||
--stage sft \
|
||||
--do_train \
|
||||
--model_name_or_path meta-llama/Llama-2-7b-hf \
|
||||
--dataset alpaca_gpt4_en,glaive_toolcall \
|
||||
--dataset_dir ../../data \
|
||||
--template default \
|
||||
--finetuning_type lora \
|
||||
--lora_target q_proj,v_proj \
|
||||
--output_dir ../../saves/LLaMA2-7B/lora/sft \
|
||||
--overwrite_cache \
|
||||
--overwrite_output_dir \
|
||||
--cutoff_len 1024 \
|
||||
--preprocessing_num_workers 16 \
|
||||
--max_samples 3000 \
|
||||
--tokenized_path ../../saves/datasets/sft
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
|
||||
--stage pt \
|
||||
--do_train \
|
||||
--model_name_or_path meta-llama/Llama-2-7b-hf \
|
||||
--dataset c4_demo \
|
||||
--dataset_dir ../../data \
|
||||
--finetuning_type lora \
|
||||
--lora_target q_proj,v_proj \
|
||||
--output_dir ../../saves/LLaMA2-7B/lora/pretrain \
|
||||
--overwrite_cache \
|
||||
--overwrite_output_dir \
|
||||
--cutoff_len 1024 \
|
||||
--preprocessing_num_workers 16 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--gradient_accumulation_steps 8 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_steps 10 \
|
||||
--warmup_steps 20 \
|
||||
--save_steps 100 \
|
||||
--eval_steps 100 \
|
||||
--evaluation_strategy steps \
|
||||
--load_best_model_at_end \
|
||||
--learning_rate 5e-5 \
|
||||
--num_train_epochs 3.0 \
|
||||
--max_samples 10000 \
|
||||
--val_size 0.1 \
|
||||
--plot_loss \
|
||||
--fp16
|
||||
|
|
@ -1,33 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
|
||||
--stage rm \
|
||||
--do_train \
|
||||
--model_name_or_path meta-llama/Llama-2-7b-hf \
|
||||
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
|
||||
--create_new_adapter \
|
||||
--dataset orca_rlhf \
|
||||
--dataset_dir ../../data \
|
||||
--template default \
|
||||
--finetuning_type lora \
|
||||
--lora_target q_proj,v_proj \
|
||||
--output_dir ../../saves/LLaMA2-7B/lora/reward \
|
||||
--overwrite_cache \
|
||||
--overwrite_output_dir \
|
||||
--cutoff_len 1024 \
|
||||
--preprocessing_num_workers 16 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--gradient_accumulation_steps 8 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_steps 10 \
|
||||
--warmup_steps 20 \
|
||||
--save_steps 100 \
|
||||
--eval_steps 100 \
|
||||
--evaluation_strategy steps \
|
||||
--learning_rate 1e-5 \
|
||||
--num_train_epochs 1.0 \
|
||||
--max_samples 5000 \
|
||||
--val_size 0.1 \
|
||||
--plot_loss \
|
||||
--fp16
|
||||
|
|
@ -1,32 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
|
||||
--stage sft \
|
||||
--do_train \
|
||||
--model_name_or_path meta-llama/Llama-2-7b-hf \
|
||||
--dataset alpaca_gpt4_en,glaive_toolcall \
|
||||
--dataset_dir ../../data \
|
||||
--template default \
|
||||
--finetuning_type lora \
|
||||
--lora_target q_proj,v_proj \
|
||||
--output_dir ../../saves/LLaMA2-7B/lora/sft \
|
||||
--overwrite_cache \
|
||||
--overwrite_output_dir \
|
||||
--cutoff_len 1024 \
|
||||
--preprocessing_num_workers 16 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--gradient_accumulation_steps 8 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_steps 10 \
|
||||
--warmup_steps 20 \
|
||||
--save_steps 100 \
|
||||
--eval_steps 100 \
|
||||
--evaluation_strategy steps \
|
||||
--load_best_model_at_end \
|
||||
--learning_rate 5e-5 \
|
||||
--num_train_epochs 3.0 \
|
||||
--max_samples 3000 \
|
||||
--val_size 0.1 \
|
||||
--plot_loss \
|
||||
--fp16
|
||||
|
|
@ -1,33 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
|
||||
--stage sft \
|
||||
--do_train \
|
||||
--model_name_or_path llava-hf/llava-1.5-7b-hf \
|
||||
--visual_inputs \
|
||||
--dataset mllm_demo \
|
||||
--dataset_dir ../../data \
|
||||
--template vicuna \
|
||||
--finetuning_type lora \
|
||||
--lora_target q_proj,v_proj \
|
||||
--output_dir ../../saves/LLaMA2-7B/lora/sft_mllm \
|
||||
--overwrite_cache \
|
||||
--overwrite_output_dir \
|
||||
--cutoff_len 1024 \
|
||||
--preprocessing_num_workers 16 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--gradient_accumulation_steps 8 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_steps 10 \
|
||||
--warmup_steps 20 \
|
||||
--save_steps 100 \
|
||||
--eval_steps 100 \
|
||||
--evaluation_strategy steps \
|
||||
--load_best_model_at_end \
|
||||
--learning_rate 5e-5 \
|
||||
--num_train_epochs 100.0 \
|
||||
--max_samples 3000 \
|
||||
--val_size 0.1 \
|
||||
--plot_loss \
|
||||
--fp16
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
# model
|
||||
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
|
||||
template: llama3
|
||||
|
||||
# export
|
||||
export_dir: models/llama3_gptq
|
||||
export_quantization_bit: 4
|
||||
export_quantization_dataset: data/c4_demo.json
|
||||
export_size: 2
|
||||
export_device: cpu
|
||||
export_legacy_format: false
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
# Note: DO NOT use quantized model or quantization_bit when merging lora weights
|
||||
|
||||
# model
|
||||
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
|
||||
adapter_name_or_path: saves/llama3-8b/lora/sft
|
||||
template: llama3
|
||||
finetuning_type: lora
|
||||
|
||||
# export
|
||||
export_dir: models/llama3_lora_sft
|
||||
export_size: 2
|
||||
export_device: cpu
|
||||
export_legacy_format: false
|
||||
|
|
@ -1,12 +0,0 @@
|
|||
#!/bin/bash
|
||||
# DO NOT use quantized model or quantization_bit when merging lora weights
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli export \
|
||||
--model_name_or_path meta-llama/Llama-2-7b-hf \
|
||||
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
|
||||
--template default \
|
||||
--finetuning_type lora \
|
||||
--export_dir ../../models/llama2-7b-sft \
|
||||
--export_size 2 \
|
||||
--export_device cpu \
|
||||
--export_legacy_format False
|
||||
|
|
@ -1,11 +0,0 @@
|
|||
#!/bin/bash
|
||||
# NEED TO run `merge.sh` before using this script
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli export \
|
||||
--model_name_or_path ../../models/llama2-7b-sft \
|
||||
--template default \
|
||||
--export_dir ../../models/llama2-7b-sft-int4 \
|
||||
--export_quantization_bit 4 \
|
||||
--export_quantization_dataset ../../data/c4_demo.json \
|
||||
--export_size 2 \
|
||||
--export_legacy_format False
|
||||
|
|
@ -1,30 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
|
||||
--stage sft \
|
||||
--do_train \
|
||||
--model_name_or_path BlackSamorez/Llama-2-7b-AQLM-2Bit-1x16-hf \
|
||||
--dataset alpaca_gpt4_en,glaive_toolcall \
|
||||
--dataset_dir ../../data \
|
||||
--template default \
|
||||
--finetuning_type lora \
|
||||
--lora_target q_proj,v_proj \
|
||||
--output_dir ../../saves/LLaMA2-7B/lora/sft \
|
||||
--overwrite_cache \
|
||||
--overwrite_output_dir \
|
||||
--cutoff_len 1024 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--gradient_accumulation_steps 8 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_steps 10 \
|
||||
--save_steps 100 \
|
||||
--eval_steps 100 \
|
||||
--evaluation_strategy steps \
|
||||
--load_best_model_at_end \
|
||||
--learning_rate 5e-5 \
|
||||
--num_train_epochs 3.0 \
|
||||
--max_samples 3000 \
|
||||
--val_size 0.1 \
|
||||
--plot_loss \
|
||||
--fp16
|
||||
|
|
@ -1,30 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
|
||||
--stage sft \
|
||||
--do_train \
|
||||
--model_name_or_path TheBloke/Llama-2-7B-AWQ \
|
||||
--dataset alpaca_gpt4_en,glaive_toolcall \
|
||||
--dataset_dir ../../data \
|
||||
--template default \
|
||||
--finetuning_type lora \
|
||||
--lora_target q_proj,v_proj \
|
||||
--output_dir ../../saves/LLaMA2-7B/lora/sft \
|
||||
--overwrite_cache \
|
||||
--overwrite_output_dir \
|
||||
--cutoff_len 1024 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--gradient_accumulation_steps 8 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_steps 10 \
|
||||
--save_steps 100 \
|
||||
--eval_steps 100 \
|
||||
--evaluation_strategy steps \
|
||||
--load_best_model_at_end \
|
||||
--learning_rate 5e-5 \
|
||||
--num_train_epochs 3.0 \
|
||||
--max_samples 3000 \
|
||||
--val_size 0.1 \
|
||||
--plot_loss \
|
||||
--fp16
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
|
||||
--stage sft \
|
||||
--do_train \
|
||||
--model_name_or_path meta-llama/Llama-2-7b-hf \
|
||||
--dataset alpaca_gpt4_en,glaive_toolcall \
|
||||
--dataset_dir ../../data \
|
||||
--template default \
|
||||
--finetuning_type lora \
|
||||
--lora_target q_proj,v_proj \
|
||||
--output_dir ../../saves/LLaMA2-7B/lora/sft \
|
||||
--overwrite_cache \
|
||||
--overwrite_output_dir \
|
||||
--cutoff_len 1024 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--gradient_accumulation_steps 8 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_steps 10 \
|
||||
--save_steps 100 \
|
||||
--eval_steps 100 \
|
||||
--evaluation_strategy steps \
|
||||
--load_best_model_at_end \
|
||||
--learning_rate 5e-5 \
|
||||
--num_train_epochs 3.0 \
|
||||
--max_samples 3000 \
|
||||
--val_size 0.1 \
|
||||
--quantization_bit 4 \
|
||||
--plot_loss \
|
||||
--fp16
|
||||
|
|
@ -1,30 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
|
||||
--stage sft \
|
||||
--do_train \
|
||||
--model_name_or_path TheBloke/Llama-2-7B-GPTQ \
|
||||
--dataset alpaca_gpt4_en,glaive_toolcall \
|
||||
--dataset_dir ../../data \
|
||||
--template default \
|
||||
--finetuning_type lora \
|
||||
--lora_target q_proj,v_proj \
|
||||
--output_dir ../../saves/LLaMA2-7B/lora/sft \
|
||||
--overwrite_cache \
|
||||
--overwrite_output_dir \
|
||||
--cutoff_len 1024 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--gradient_accumulation_steps 8 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_steps 10 \
|
||||
--save_steps 100 \
|
||||
--eval_steps 100 \
|
||||
--evaluation_strategy steps \
|
||||
--load_best_model_at_end \
|
||||
--learning_rate 5e-5 \
|
||||
--num_train_epochs 3.0 \
|
||||
--max_samples 3000 \
|
||||
--val_size 0.1 \
|
||||
--plot_loss \
|
||||
--fp16
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
stage: sft
|
||||
do_train: true
|
||||
model_name_or_path: BlackSamorez/Llama-2-7b-AQLM-2Bit-1x16-hf
|
||||
dataset: alpaca_gpt4_en,glaive_toolcall
|
||||
dataset_dir: data
|
||||
template: default
|
||||
finetuning_type: lora
|
||||
lora_target: q_proj,v_proj
|
||||
output_dir: ../../saves/LLaMA2-7B/lora/sft
|
||||
overwrite_cache: true
|
||||
overwrite_output_dir: true
|
||||
cutoff_len: 1024
|
||||
per_device_train_batch_size: 1
|
||||
per_device_eval_batch_size: 1
|
||||
gradient_accumulation_steps: 8
|
||||
lr_scheduler_type: cosine
|
||||
logging_steps: 10
|
||||
save_steps: 100
|
||||
eval_steps: 100
|
||||
evaluation_strategy: steps
|
||||
load_best_model_at_end: true
|
||||
learning_rate: 5e-5
|
||||
num_train_epochs: 3.0
|
||||
max_samples: 3000
|
||||
val_size: 0.1
|
||||
plot_loss: true
|
||||
fp16: true
|
||||
6
setup.py
6
setup.py
|
|
@ -20,12 +20,12 @@ def get_requires():
|
|||
|
||||
|
||||
extra_require = {
|
||||
"deepspeed": ["deepspeed>=0.10.0"],
|
||||
"metrics": ["nltk", "jieba", "rouge-chinese"],
|
||||
"deepspeed": ["deepspeed>=0.10.0"],
|
||||
"bitsandbytes": ["bitsandbytes>=0.39.0"],
|
||||
"vllm": ["vllm>=0.4.0"],
|
||||
"galore": ["galore-torch"],
|
||||
"badam": ["badam"],
|
||||
"vllm": ["vllm>=0.4.0"],
|
||||
"bitsandbytes": ["bitsandbytes>=0.39.0"],
|
||||
"gptq": ["optimum>=1.16.0", "auto-gptq>=0.5.0"],
|
||||
"awq": ["autoawq"],
|
||||
"aqlm": ["aqlm[gpu]>=1.1.0"],
|
||||
|
|
|
|||
|
|
@ -0,0 +1,9 @@
|
|||
from llmtuner.webui.interface import create_ui
|
||||
|
||||
|
||||
def main():
|
||||
create_ui().queue().launch(server_name="0.0.0.0", server_port=None, share=False)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue