From 073d28968c22b80bd98e865b00cad5ebd38f447c Mon Sep 17 00:00:00 2001 From: fanqiNO1 <1848839264@qq.com> Date: Sat, 2 Mar 2024 21:26:52 +0800 Subject: [PATCH] [Fix] Fix comments --- docs/en/get_started/quickstart.md | 89 ++++++++++++++++++--------- docs/zh_cn/get_started/quickstart.md | 91 +++++++++++++++++++--------- 2 files changed, 124 insertions(+), 56 deletions(-) diff --git a/docs/en/get_started/quickstart.md b/docs/en/get_started/quickstart.md index 6c30d348c..23198bf3b 100644 --- a/docs/en/get_started/quickstart.md +++ b/docs/en/get_started/quickstart.md @@ -4,7 +4,7 @@ In this section, we will show you how to use XTuner to fine-tune a model to help After installing XTuner successfully, we can start fine-tuning the model. In this section, we will demonstrate how to use XTuner to apply the QLoRA algorithm to fine-tune InternLM2-Chat-7B on the Colorist dataset. -The Colorist dataset is a dataset that provides color choices and suggestions based on color descriptions. A model fine-tuned on this dataset can be used to give a hexadecimal color code based on the user's description of the color. For example, when the user enters "a calming but fairly bright light sky blue, between sky blue and baby blue, with a hint of fluorescence due to its brightness", the model will output ![#66ccff](https://img.shields.io/badge/%2366ccff-66CCFF), which matches the user's description. There are a few sample data from this dataset: +The Colorist dataset ([HuggingFace link](https://huggingface.co/datasets/burkelibbey/colors); [ModelScope link](https://www.modelscope.cn/datasets/fanqiNO1/colors/summary)) is a dataset that provides color choices and suggestions based on color descriptions. A model fine-tuned on this dataset can be used to give a hexadecimal color code based on the user's description of the color. For example, when the user enters "a calming but fairly bright light sky blue, between sky blue and baby blue, with a hint of fluorescence due to its brightness", the model will output ![#66ccff](https://img.shields.io/badge/%2366ccff-66CCFF), which matches the user's description. There are a few sample data from this dataset: | Enligsh Description | Chinese Description | Color | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------ | @@ -14,10 +14,26 @@ The Colorist dataset is a dataset that provides color choices and suggestions ba ## Prepare the model weights -Before fine-tuning the model, we first need to prepare the weights of the model. Since XTuner pulls model weights from HuggingFace by default, which may lead to an unstable download process, slow download speed and other problems, we choose to download the weights of InternLM2-Chat-7B from ModelScope. +Before fine-tuning the model, we first need to prepare the weights of the model. + +### Download from HuggingFace ```bash -pip install modelscope +pip install -U huggingface_hub + +# Download the model weights to Shanghai_AI_Laboratory/internlm2-chat-7b +huggingface-cli download internlm/internlm2-chat-7b \ + --local-dir Shanghai_AI_Laboratory/internlm2-chat-7b \ + --local-dir-use-symlinks False \ + --resume-download +``` + +### Download from ModelScope + +Since pulling model weights from HuggingFace may lead to an unstable download process, slow download speed and other problems, we can choose to download the weights of InternLM2-Chat-7B from ModelScope when experiencing network issues. + +```bash +pip install -U modelscope # Download the model weights to the current directory python -c "from modelscope import snapshot_download; snapshot_download('Shanghai_AI_Laboratory/internlm2-chat-7b', cache_dir='.')" @@ -32,7 +48,15 @@ The HuggingFace link and ModelScope link are attached here: ## Prepare the fine-tuning dataset -Due to the same reason, we choose to download the dataset from ModelScope. +### Download from HuggingFace + +```bash +git clone https://huggingface.co/datasets/burkelibbey/colors +``` + +### Download from ModelScope + +Due to the same reason, we can choose to download the dataset from ModelScope. ```bash git clone https://www.modelscope.cn/datasets/fanqiNO1/colors.git @@ -99,19 +123,19 @@ The directory structure at this point should look like this: In this step, we need to modify the model path and dataset path to local paths and modify the dataset loading method. In addition, since the copied config is based on the Base model, we also need to modify the `prompt_template` to adapt to the Chat model. -```python +```diff ####################################################################### # PART 1 Settings # ####################################################################### # Model -# pretrained_model_name_or_path = 'internlm/internlm2-7b' -pretrained_model_name_or_path = './Shanghai_AI_Laboratory/internlm2-chat-7b' +- pretrained_model_name_or_path = 'internlm/internlm2-7b' ++ pretrained_model_name_or_path = './Shanghai_AI_Laboratory/internlm2-chat-7b' # Data -# data_path = 'burkelibbey/colors' -data_path = './colors/train.jsonl' -# prompt_template = PROMPT_TEMPLATE.default -prompt_template = PROMPT_TEMPLATE.internlm2_chat +- data_path = 'burkelibbey/colors' ++ data_path = './colors/train.jsonl' +- prompt_template = PROMPT_TEMPLATE.default ++ prompt_template = PROMPT_TEMPLATE.internlm2_chat ... ####################################################################### @@ -119,8 +143,8 @@ prompt_template = PROMPT_TEMPLATE.internlm2_chat ####################################################################### train_dataset = dict( type=process_hf_dataset, - # dataset=dict(type=load_dataset, path=data_path), - dataset=dict(type=load_dataset, path='json', data_files=dict(train=data_path)), +- dataset=dict(type=load_dataset, path=data_path), ++ dataset=dict(type=load_dataset, path='json', data_files=dict(train=data_path)), tokenizer=tokenizer, max_length=max_length, dataset_map_fn=colors_map_fn, @@ -139,11 +163,11 @@ Once having done the above steps, we can start fine-tuning using the following c ```bash # Single GPU -xtuner train internlm2_7b_qlora_colorist_e5_copy.py +xtuner train ./internlm2_7b_qlora_colorist_e5_copy.py # Multiple GPUs -NPROC_PER_NODE=${GPU_NUM} xtuner train internlm2_7b_qlora_colorist_e5_copy.py +NPROC_PER_NODE=${GPU_NUM} xtuner train ./internlm2_7b_qlora_colorist_e5_copy.py # Slurm -srun ${SRUN_ARGS} xtuner train internlm2_7b_qlora_colorist_e5_copy.py --launcher slurm +srun ${SRUN_ARGS} xtuner train ./internlm2_7b_qlora_colorist_e5_copy.py --launcher slurm ``` The correct training log may look similar to the one shown below: @@ -215,36 +239,36 @@ It is clear that the output of the model after training has been fully aligned w # Model Convert + LoRA Merge -After training, we will get several pth files that do **not** contain all the parameters of the model, but store the parameters updated by the training process of the QLoRA algorithm. Therefore, we need to convert these pth files to hf format and merge them into the original weights. +After training, we will get several `.pth` files that do **NOT** contain all the parameters of the model, but store the parameters updated by the training process of the QLoRA algorithm. Therefore, we need to convert these `.pth` files to HuggingFace format and merge them into the original LLM weights. ### Model Convert -XTuner has already integrated the function of converting the model to hf format. We can use the following command to convert the model. +XTuner has already integrated the tool of converting the model to HuggingFace format. We can use the following command to convert the model. ```bash # Create the directory to store parameters in hf format mkdir work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720_hf # Convert the model to hf format -xtuner convert pth_to_hf internlm2_7b_qlora_colorist_e5_copy.py\ - work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720.pth\ +xtuner convert pth_to_hf internlm2_7b_qlora_colorist_e5_copy.py \ + work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720.pth \ work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720_hf ``` -This command will convert `work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720.pth` to hf format based on the contents of the config `internlm2_7b_qlora_colorist_e5_copy/iter_720.pth` and will save it in `work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720_hf`. +This command will convert `work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720.pth` to hf format based on the contents of the config `internlm2_7b_qlora_colorist_e5_copy.py` and will save it in `work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720_hf`. ### LoRA Merge -XTuner has also integrated the function of merging weights, we just need to execute the following command: +XTuner has also integrated the tool of merging LoRA weights, we just need to execute the following command: ```bash # Create the directory to store the merged weights mkdir work_dirs/internlm2_7b_qlora_colorist_e5_copy/merged # Merge the weights -xtuner convert merge Shanghai_AI_Laboratory/internlm2-chat-7b\ - work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720_hf\ - work_dirs/internlm2_7b_qlora_colorist_e5_copy/merged\ +xtuner convert merge Shanghai_AI_Laboratory/internlm2-chat-7b \ + work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720_hf \ + work_dirs/internlm2_7b_qlora_colorist_e5_copy/merged \ --max-shard-size 2GB ``` @@ -252,11 +276,20 @@ Similar to the command above, this command will read the original parameter path ## Chat with the model -To better appreciate the model's capabilities after merging the weights, we can chat with the model. XTuner also integrates the function of chatting with the merged model. We can start a simple demo to chat with the model with the following command: +To better appreciate the model's capabilities after merging the weights, we can chat with the model. XTuner also integrates the tool of chatting with models. We can start a simple demo to chat with the model with the following command: + +```bash +xtuner chat work_dirs/internlm2_7b_qlora_colorist_e5_copy/merged \ + --prompt-template internlm2_chat \ + --system-template colorist +``` + +Of course, we can also choose not to merge the weights and instead chat directly with the LLM + LoRA Adapter, we just need to execute the following command: ```bash -xtuner chat work_dirs/internlm2_7b_qlora_colorist_e5_copy/merged\ - --prompt-template internlm2_chat\ +xtuner chat Shanghai_AI_Laboratory/internlm2-chat-7b + --adapter work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720_hf \ + --prompt-template internlm2_chat \ --system-template colorist ``` diff --git a/docs/zh_cn/get_started/quickstart.md b/docs/zh_cn/get_started/quickstart.md index 4ac4bbeb9..1fa728cdf 100644 --- a/docs/zh_cn/get_started/quickstart.md +++ b/docs/zh_cn/get_started/quickstart.md @@ -4,7 +4,7 @@ 在成功安装 XTuner 后，便可以开始进行模型的微调。在本节中，我们将演示如何使用 XTuner，应用 QLoRA 算法在 Colorist 数据集上微调 InternLM2-Chat-7B。 -Colorist 数据集是一个根据颜色描述提供颜色选择与建议的数据集，经过该数据集微调的模型可以做到根据用户对于颜色的描述，从而给出16进制下的颜色编码，如用户输入“宁静而又相当明亮的浅天蓝色，介于天蓝色和婴儿蓝之间，因其亮度而带有一丝轻微的荧光感。”，模型输出 ![#66ccff](https://img.shields.io/badge/%2366ccff-66CCFF)，该颜色很符合用户的描述。以下是该数据集的几条样例数据： +Colorist 数据集（[HuggingFace 链接](https://huggingface.co/datasets/burkelibbey/colors)；[ModelScope 链接](https://www.modelscope.cn/datasets/fanqiNO1/colors/summary)）是一个根据颜色描述提供颜色选择与建议的数据集，经过该数据集微调的模型可以做到根据用户对于颜色的描述，从而给出16进制下的颜色编码，如用户输入“宁静而又相当明亮的浅天蓝色，介于天蓝色和婴儿蓝之间，因其亮度而带有一丝轻微的荧光感。”，模型输出 ![#66ccff](https://img.shields.io/badge/%2366ccff-66CCFF)，该颜色很符合用户的描述。以下是该数据集的几条样例数据： | 英文描述 | 中文描述 | 颜色 | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------ | @@ -14,10 +14,26 @@ Colorist 数据集是一个根据颜色描述提供颜色选择与建议的数 ## 准备模型权重 -在微调模型前，首先要准备待微调模型的权重。由于 XTuner 默认从 HuggingFace 拉取模型权重，这可能会导致下载过程不稳定、下载速度过慢等问题，因此我们选择从 ModelScope 下载 InternLM2-Chat-7B 的权重。 +在微调模型前，首先要准备待微调模型的权重。 + +### 从 HuggingFace 下载 ```bash -pip install modelscope +pip install -U huggingface_hub + +# 拉取模型至 Shanghai_AI_Laboratory/internlm2-chat-7b +huggingface-cli download internlm/internlm2-chat-7b \ + --local-dir Shanghai_AI_Laboratory/internlm2-chat-7b \ + --local-dir-use-symlinks False \ + --resume-download +``` + +### 从 ModelScope 下载 + +由于从 HuggingFace 拉取模型权重，可能存在下载过程不稳定、下载速度过慢等问题。因此在下载过程遇到网络问题时，我们则可以选择从 ModelScope 下载 InternLM2-Chat-7B 的权重。 + +```bash +pip install -U modelscope # 拉取模型至当前目录 python -c "from modelscope import snapshot_download; snapshot_download('Shanghai_AI_Laboratory/internlm2-chat-7b', cache_dir='.')" @@ -32,7 +48,17 @@ python -c "from modelscope import snapshot_download; snapshot_download('Shanghai ## 准备微调数据集 -由于相同的问题，因此我们从 ModelScope 下载所需要的微调数据集。 +接下来，我们需要准备微调数据集。 + +### 从 HuggingFace 下载 + +```bash +git clone https://huggingface.co/datasets/burkelibbey/colors +``` + +### 从 ModelScope 下载 + +由于相同的问题，因此我们可以选择从 ModelScope 下载所需要的微调数据集。 ```bash git clone https://www.modelscope.cn/datasets/fanqiNO1/colors.git @@ -99,19 +125,19 @@ xtuner copy-cfg internlm2_7b_qlora_colorist_e5 . 在这一步中，我们需要修改待微调模型路径和数据路径为本地路径，并且修改数据集加载方式。 此外，由于复制得到的配置文件是基于基座（Base）模型的，所以还需要修改 `prompt_template` 以适配对话（Chat）模型。 -```python +```diff ####################################################################### # PART 1 Settings # ####################################################################### # Model -# pretrained_model_name_or_path = 'internlm/internlm2-7b' -pretrained_model_name_or_path = './Shanghai_AI_Laboratory/internlm2-chat-7b' +- pretrained_model_name_or_path = 'internlm/internlm2-7b' ++ pretrained_model_name_or_path = './Shanghai_AI_Laboratory/internlm2-chat-7b' # Data -# data_path = 'burkelibbey/colors' -data_path = './colors/train.jsonl' -# prompt_template = PROMPT_TEMPLATE.default -prompt_template = PROMPT_TEMPLATE.internlm2_chat +- data_path = 'burkelibbey/colors' ++ data_path = './colors/train.jsonl' +- prompt_template = PROMPT_TEMPLATE.default ++ prompt_template = PROMPT_TEMPLATE.internlm2_chat ... ####################################################################### @@ -119,8 +145,8 @@ prompt_template = PROMPT_TEMPLATE.internlm2_chat ####################################################################### train_dataset = dict( type=process_hf_dataset, - # dataset=dict(type=load_dataset, path=data_path), - dataset=dict(type=load_dataset, path='json', data_files=dict(train=data_path)), +- dataset=dict(type=load_dataset, path=data_path), ++ dataset=dict(type=load_dataset, path='json', data_files=dict(train=data_path)), tokenizer=tokenizer, max_length=max_length, dataset_map_fn=colors_map_fn, @@ -138,12 +164,12 @@ train_dataset = dict( 在完成上述操作后，便可以使用下面的指令启动微调任务了。 ```bash -# 单张 GPU -xtuner train internlm2_7b_qlora_colorist_e5_copy.py +# 单机单卡 +xtuner train ./internlm2_7b_qlora_colorist_e5_copy.py # 单机多卡 -NPROC_PER_NODE=${GPU_NUM} xtuner train internlm2_7b_qlora_colorist_e5_copy.py +NPROC_PER_NODE=${GPU_NUM} xtuner train ./internlm2_7b_qlora_colorist_e5_copy.py # slurm 情况 -srun ${SRUN_ARGS} xtuner train internlm2_7b_qlora_colorist_e5_copy.py --launcher slurm +srun ${SRUN_ARGS} xtuner train ./internlm2_7b_qlora_colorist_e5_copy.py --launcher slurm ``` 正确输出的训练日志应类似如下所示： @@ -215,19 +241,19 @@ Please give me a clear blue like the sky.<|im_end|> ## 模型转换 + LoRA 合并 -在训练完成后，我们会得到几个 pth 文件，这些文件存储了 QLoRA 算法训练过程所更新的参数，而非模型的全部参数。因此我们需要将这些 pth 文件转换为 hf 格式，并合并入原始权重中。 +在训练完成后，我们会得到几个 `.pth` 文件，这些文件存储了 QLoRA 算法训练过程所更新的参数，而**不是**模型的全部参数。因此我们需要将这些 `.pth` 文件转换为 HuggingFace 格式，并合并入原始的语言模型权重中。 ### 模型转换 -XTuner 已经集成好了转换为 hf 格式的功能，我们只需要执行 +XTuner 已经集成好了将模型转换为 HuggingFace 格式的工具，我们只需要执行 ```bash # 创建存放 hf 格式参数的目录 mkdir work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720_hf # 转换格式 -xtuner convert pth_to_hf internlm2_7b_qlora_colorist_e5_copy.py\ - work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720.pth\ +xtuner convert pth_to_hf internlm2_7b_qlora_colorist_e5_copy.py \ + work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720.pth \ work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720_hf ``` @@ -235,16 +261,16 @@ xtuner convert pth_to_hf internlm2_7b_qlora_colorist_e5_copy.py\ ### LoRA 合并 -XTuner 也已经集成好了合并权重的功能，我们只需执行如下指令： +XTuner 也已经集成好了合并 LoRA 权重的工具，我们只需执行如下指令： ```bash # 创建存放合并后的参数的目录 mkdir work_dirs/internlm2_7b_qlora_colorist_e5_copy/merged # 合并参数 -xtuner convert merge Shanghai_AI_Laboratory/internlm2-chat-7b\ - work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720_hf\ - work_dirs/internlm2_7b_qlora_colorist_e5_copy/merged\ +xtuner convert merge Shanghai_AI_Laboratory/internlm2-chat-7b \ + work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720_hf \ + work_dirs/internlm2_7b_qlora_colorist_e5_copy/merged \ --max-shard-size 2GB ``` @@ -252,11 +278,20 @@ xtuner convert merge Shanghai_AI_Laboratory/internlm2-chat-7b\ ## 与模型对话 -在合并权重后，为了更好地体会到模型的能力，XTuner 也集成了与合并后的模型对话的功能。通过如下命令，便可以启动一个与模型对话的简易 Demo。 +在合并权重后，为了更好地体会到模型的能力，XTuner 也集成了与模型对话的工具。通过如下命令，便可以启动一个与模型对话的简易 Demo。 + +```bash +xtuner chat work_dirs/internlm2_7b_qlora_colorist_e5_copy/merged \ + --prompt-template internlm2_chat \ + --system-template colorist +``` + +当然，我们也可以选择不合并权重，而是直接与 LLM + LoRA Adapter 进行对话，我们只需要执行如下指令： ```bash -xtuner chat work_dirs/internlm2_7b_qlora_colorist_e5_copy/merged\ - --prompt-template internlm2_chat\ +xtuner chat Shanghai_AI_Laboratory/internlm2-chat-7b + --adapter work_dirs/internlm2_7b_qlora_colorist_e5_copy/iter_720_hf \ + --prompt-template internlm2_chat \ --system-template colorist ```