[Docs] Improve README (#326)

* update

* Update README.md

* Update README.md

* Update README.md

* Update README_zh-CN.md

* update

* update

* fix pre-commit

* update

README.md

@@ -36,11 +36,26 @@ English | [简体中文](README_zh-CN.md)
 
 ## 📖 Introduction
 
-XTuner is a toolkit for efficiently fine-tuning LLM, developed by the [MMRazor](https://github.com/open-mmlab/mmrazor) and [MMDeploy](https://github.com/open-mmlab/mmdeploy) teams.
+XTuner is an efficient, flexible and full-featured toolkit for fine-tuning large models.
 
-- **Efficiency**: Support LLM fine-tuning on consumer-grade GPUs. The minimum GPU memory required for 7B LLM fine-tuning is only **8GB**, indicating that users can use nearly any GPU (even the free resource, *e.g.*, Colab) to fine-tune custom LLMs.
-- **Versatile**: Support various **LLMs** ([InternLM](https://huggingface.co/internlm), [Llama2](https://huggingface.co/meta-llama), [ChatGLM](https://huggingface.co/THUDM), [Qwen](https://huggingface.co/Qwen), [Baichuan2](https://huggingface.co/baichuan-inc), ...), **datasets** ([MOSS_003_SFT](https://huggingface.co/datasets/fnlp/moss-003-sft-data), [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), [WizardLM](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k), [oasst1](https://huggingface.co/datasets/timdettmers/openassistant-guanaco), [Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus), [Code Alpaca](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K), [Colorist](https://huggingface.co/datasets/burkelibbey/colors), ...) and **algorithms** ([QLoRA](http://arxiv.org/abs/2305.14314), [LoRA](http://arxiv.org/abs/2106.09685)), allowing users to choose the most suitable solution for their requirements.
-- **Compatibility**: Compatible with [DeepSpeed](https://github.com/microsoft/DeepSpeed) 🚀 and [HuggingFace](https://huggingface.co) 🤗 training pipeline, enabling effortless integration and utilization.
+**Efficient**
+
+- Support LLM, VLM pre-training / fine-tuning on almost all GPUs. XTuner is capable of fine-tuning 7B LLM on a single 8GB GPU, as well as multi-node fine-tuning of models exceeding 70B.
+- Automatically dispatch high-performance operators such as FlashAttention and Triton kernels to increase training throughput.
+- Compatible with [DeepSpeed](https://github.com/microsoft/DeepSpeed) 🚀, easily utilizing a variety of ZeRO optimization techniques.
+
+**Flexible**
+
+- Support various LLMs ([InternLM](https://huggingface.co/internlm), [Mixtral-8x7B](https://huggingface.co/mistralai), [Llama2](https://huggingface.co/meta-llama), [ChatGLM](https://huggingface.co/THUDM), [Qwen](https://huggingface.co/Qwen), [Baichuan](https://huggingface.co/baichuan-inc), ...).
+- Support VLM ([LLaVA](https://github.com/haotian-liu/LLaVA)). The performance of [LLaVA-InternLM2-20B](https://huggingface.co/xtuner/llava-internlm2-20b) is outstanding.
+- Well-designed data pipeline, accommodating datasets in any format, including but not limited to open-source and custom formats.
+- Support various training algorithms ([QLoRA](http://arxiv.org/abs/2305.14314), [LoRA](http://arxiv.org/abs/2106.09685), full-parameter fune-tune), allowing users to choose the most suitable solution for their requirements.
+
+**Full-featured**
+
+- Support continuous pre-training, instruction fine-tuning, and agent fine-tuning.
+- Support chatting with large models with pre-defined templates.
+- The output models can seamlessly integrate with deployment and server toolkit ([LMDeploy](https://github.com/InternLM/lmdeploy)), and large-scale evaluation toolkit ([OpenCompass](https://github.com/open-compass/opencompass), [VLMEvalKit](https://github.com/open-compass/VLMEvalKit)).
 
 ## 🌟 Demos
 
@@ -182,6 +197,7 @@ XTuner supports the efficient fine-tune (*e.g.*, QLoRA) for LLMs. Dataset prepar
 
   ```shell
   xtuner copy-cfg ${CONFIG_NAME} ${SAVE_PATH}
+  vi ${SAVE_PATH}/${CONFIG_NAME}_copy.py
   ```
 
 - **Step 1**, start fine-tuning.
@@ -190,14 +206,14 @@ XTuner supports the efficient fine-tune (*e.g.*, QLoRA) for LLMs. Dataset prepar
   xtuner train ${CONFIG_NAME_OR_PATH}
   ```
 
-  For example, we can start the QLoRA fine-tuning of InternLM-7B with oasst1 dataset by
+  For example, we can start the QLoRA fine-tuning of InternLM2-Chat-7B with oasst1 dataset by
 
   ```shell
   # On a single GPU
-  xtuner train internlm_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
+  xtuner train internlm2_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
   # On multiple GPUs
-  (DIST) NPROC_PER_NODE=${GPU_NUM} xtuner train internlm_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
-  (SLURM) srun ${SRUN_ARGS} xtuner train internlm_7b_qlora_oasst1_e3 --launcher slurm --deepspeed deepspeed_zero2
+  (DIST) NPROC_PER_NODE=${GPU_NUM} xtuner train internlm2_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
+  (SLURM) srun ${SRUN_ARGS} xtuner train internlm2_chat_7b_qlora_oasst1_e3 --launcher slurm --deepspeed deepspeed_zero2
   ```
 
   - `--deepspeed` means using [DeepSpeed](https://github.com/microsoft/DeepSpeed) 🚀 to optimize the training. XTuner comes with several integrated strategies including ZeRO-1, ZeRO-2, and ZeRO-3. If you wish to disable this feature, simply remove this argument.
@@ -220,16 +236,16 @@ xtuner chat ${NAME_OR_PATH_TO_LLM} --adapter {NAME_OR_PATH_TO_ADAPTER} [optional
 
 For example, we can start the chat with
 
-InternLM-7B with adapter trained from Alpaca-enzh:
+InternLM2-Chat-7B with adapter trained from oasst1 dataset:
 
 ```shell
-xtuner chat internlm/internlm-7b --adapter xtuner/internlm-7b-qlora-alpaca-enzh --prompt-template internlm_chat --system-template alpaca
+xtuner chat internlm/internlm2-chat-7b --adapter xtuner/internlm2-chat-7b-qlora-oasst1 --prompt-template internlm2_chat
 ```
 
-Llama2-7b with adapter trained from MOSS-003-SFT:
+LLaVA-InternLM2-7B:
 
 ```shell
-xtuner chat meta-llama/Llama-2-7b-hf --adapter xtuner/Llama-2-7b-qlora-moss-003-sft --bot-name Llama2 --prompt-template moss_sft --system-template moss_sft --with-plugins calculate solve search --no-streamer
+xtuner chat internlm/internlm2-chat-7b --visual-encoder openai/clip-vit-large-patch14-336 --llava xtuner/llava-internlm2-7b --prompt-template internlm2_chat --image $IMAGE_PATH
 ```
 
 For more examples, please see [chat.md](./docs/en/user_guides/chat.md).

README_zh-CN.md

@@ -25,7 +25,7 @@
 
 - **\[2024/01\]** 支持 [InternLM2](https://huggingface.co/internlm) 模型！同时，最新版的多模态大模型 [LLaVA-Internlm2-7B](https://huggingface.co/xtuner/llava-internlm2-7b) / [20B](https://huggingface.co/xtuner/llava-internlm2-20b) 发布，其表现出强大的性能！
 - **\[2024/01\]** 支持 [DeepSeek-MoE](https://huggingface.co/deepseek-ai/deepseek-moe-16b-chat) 模型！20GB 显存即可实现 QLoRA 微调，4x80GB 即可实现全参数微调。快速开始请查阅相关[配置文件](xtuner/configs/deepseek/)！
-- **\[2023/12\]** 🔥 支持多模态模型 VLM（[LLaVA-v1.5](https://github.com/haotian-liu/LLaVA)）预训练和指令微调！快速开始请查阅此[文档](xtuner/configs/llava/README_zh.md)！
+- **\[2023/12\]** 🔥 支持多模态模型 VLM（[LLaVA-v1.5](https://github.com/haotian-liu/LLaVA)）预训练和指令微调！快速开始请查阅此[文档](xtuner/configs/llava/README_zh-CN.md)！
 - **\[2023/12\]** 🔥 支持 [Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) 模型！快速开始请查阅此[文档](xtuner/configs/mixtral/README.md)！
 - **\[2023/11\]** 支持 [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b) 模型！
 - **\[2023/10\]** 支持 [MSAgent-Bench](https://modelscope.cn/datasets/damo/MSAgent-Bench) 数据集，并且微调所得大语言模型可应用至 [Lagent](https://github.com/InternLM/lagent) 框架！
@@ -36,11 +36,26 @@
 
 ## 📖 介绍
 
-XTuner 是一个轻量级微调大语言模型的工具库，由 [MMRazor](https://github.com/open-mmlab/mmrazor) 和 [MMDeploy](https://github.com/open-mmlab/mmdeploy) 团队联合开发。
+XTuner 是一个高效、灵活、全能的轻量化大模型微调工具库。
 
-- **轻量级**: 支持在消费级显卡上微调大语言模型。对于 7B 参数量，微调所需的最小显存仅为 **8GB**，这使得用户可以使用几乎任何显卡（甚至免费资源，例如Colab）来微调获得自定义大语言模型助手。
-- **多样性**: 支持多种**大语言模型**（[InternLM](https://huggingface.co/internlm)、[Llama2](https://huggingface.co/meta-llama)、[ChatGLM](https://huggingface.co/THUDM)、[Qwen](https://huggingface.co/Qwen)、[Baichuan2](https://huggingface.co/baichuan-inc), ...），**数据集**（[MOSS_003_SFT](https://huggingface.co/datasets/fnlp/moss-003-sft-data), [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), [WizardLM](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k), [oasst1](https://huggingface.co/datasets/timdettmers/openassistant-guanaco), [Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus), [Code Alpaca](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K), [Colorist](https://huggingface.co/datasets/burkelibbey/colors), ...）和**微调算法**（[QLoRA](http://arxiv.org/abs/2305.14314)、[LoRA](http://arxiv.org/abs/2106.09685)），支撑用户根据自身具体需求选择合适的解决方案。
-- **兼容性**: 兼容 [DeepSpeed](https://github.com/microsoft/DeepSpeed) 🚀 和 [HuggingFace](https://huggingface.co) 🤗 的训练流程，支撑用户无感式集成与使用。
+**高效**
+
+- 支持大语言模型 LLM、多模态图文模型 VLM 的预训练及轻量级微调。XTuner 支持在 8GB 显存下微调 7B 模型，同时也支持多节点跨设备微调更大尺度模型（70B+）。
+- 自动分发高性能算子（如 FlashAttention、Triton kernels 等）以加速训练吞吐。
+- 兼容 [DeepSpeed](https://github.com/microsoft/DeepSpeed) 🚀，轻松应用各种 ZeRO 训练优化策略。
+
+**灵活**
+
+- 支持多种大语言模型，包括但不限于 [InternLM](https://huggingface.co/internlm)、[Mixtral-8x7B](https://huggingface.co/mistralai)、[Llama2](https://huggingface.co/meta-llama)、[ChatGLM](https://huggingface.co/THUDM)、[Qwen](https://huggingface.co/Qwen)、[Baichuan](https://huggingface.co/baichuan-inc)。
+- 支持多模态图文模型 LLaVA 的预训练与微调。利用 XTuner 训得模型 [LLaVA-InternLM2-20B](https://huggingface.co/xtuner/llava-internlm2-20b) 表现优异。
+- 精心设计的数据管道，兼容任意数据格式，开源数据或自定义数据皆可快速上手。
+- 支持 [QLoRA](http://arxiv.org/abs/2305.14314)、[LoRA](http://arxiv.org/abs/2106.09685)、全量参数微调等多种微调算法，支撑用户根据具体需求作出最优选择。
+
+**全能**
+
+- 支持增量预训练、指令微调与 Agent 微调。
+- 预定义众多开源对话模版，支持与开源或训练所得模型进行对话。
+- 训练所得模型可无缝接入部署工具库 [LMDeploy](https://github.com/InternLM/lmdeploy)、大规模评测工具库 [OpenCompass](https://github.com/open-compass/opencompass) 及 [VLMEvalKit](https://github.com/open-compass/VLMEvalKit)。
 
 ## 🌟 示例
 
@@ -182,6 +197,7 @@ XTuner 支持微调大语言模型。数据集预处理指南请查阅[文档](.
 
   ```shell
   xtuner copy-cfg ${CONFIG_NAME} ${SAVE_PATH}
+  vi ${SAVE_PATH}/${CONFIG_NAME}_copy.py
   ```
 
 - **步骤 1**，开始微调。
@@ -190,14 +206,14 @@ XTuner 支持微调大语言模型。数据集预处理指南请查阅[文档](.
   xtuner train ${CONFIG_NAME_OR_PATH}
   ```
 
-  例如，我们可以利用 QLoRA 算法在 oasst1 数据集上微调 InternLM-7B：
+  例如，我们可以利用 QLoRA 算法在 oasst1 数据集上微调 InternLM2-Chat-7B：
 
   ```shell
   # 单卡
-  xtuner train internlm_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
+  xtuner train internlm2_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
   # 多卡
-  (DIST) NPROC_PER_NODE=${GPU_NUM} xtuner train internlm_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
-  (SLURM) srun ${SRUN_ARGS} xtuner train internlm_7b_qlora_oasst1_e3 --launcher slurm --deepspeed deepspeed_zero2
+  (DIST) NPROC_PER_NODE=${GPU_NUM} xtuner train internlm2_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
+  (SLURM) srun ${SRUN_ARGS} xtuner train internlm2_chat_7b_qlora_oasst1_e3 --launcher slurm --deepspeed deepspeed_zero2
   ```
 
   - `--deepspeed` 表示使用 [DeepSpeed](https://github.com/microsoft/DeepSpeed) 🚀 来优化训练过程。XTuner 内置了多种策略，包括 ZeRO-1、ZeRO-2、ZeRO-3 等。如果用户期望关闭此功能，请直接移除此参数。
@@ -220,16 +236,16 @@ xtuner chat ${NAME_OR_PATH_TO_LLM} --adapter {NAME_OR_PATH_TO_ADAPTER} [optional
 
 例如：
 
-与 InternLM-7B + Alpaca-enzh adapter 对话：
+与 InternLM2-Chat-7B, oasst1 adapter 对话：
 
 ```shell
-xtuner chat internlm/internlm-7b --adapter xtuner/internlm-7b-qlora-alpaca-enzh --prompt-template internlm_chat --system-template alpaca
+xtuner chat internlm/internlm2-chat-7b --adapter xtuner/internlm2-chat-7b-qlora-oasst1 --prompt-template internlm2_chat
 ```
 
-与 Llama2-7b + MOSS-003-SFT adapter 对话：
+与 LLaVA-InternLM2-7B 对话：
 
 ```shell
-xtuner chat meta-llama/Llama-2-7b-hf --adapter xtuner/Llama-2-7b-qlora-moss-003-sft --bot-name Llama2 --prompt-template moss_sft --system-template moss_sft --with-plugins calculate solve search --no-streamer
+xtuner chat internlm/internlm2-chat-7b --visual-encoder openai/clip-vit-large-patch14-336 --llava xtuner/llava-internlm2-7b --prompt-template internlm2_chat --image $IMAGE_PATH
 ```
 
 更多示例，请查阅[文档](./docs/zh_cn/user_guides/chat.md)。

setup.py

@@ -99,7 +99,8 @@ def gen_packages_items():
     setup(
         name='xtuner',
         version=get_version(),
-        description='A toolkit for efficiently fine-tuning LLM',
+        description=('An efficient, flexible and full-featured toolkit for '
+                     'fine-tuning large models'),
         long_description=readme(),
         long_description_content_type='text/markdown',
         author='XTuner Contributors',

xtuner/configs/llava/README.md

@@ -1,5 +1,7 @@
 # LLaVA Full Pipeline
 
+English | [简体中文](./README_zh-CN.md)
+
 ## Results
 
 XTuner primarily promotes the LLM-QLoRA / ViT-LoRA LLaVA architecture, and the evaluation results on various datasets are as follows:

xtuner/configs/llava/README_zh.md → xtuner/configs/llava/README_zh-CN.md

@@ -1,5 +1,7 @@
 # LLaVA 全流程
 
+[English](./README.md) | 简体中文
+
 ## 结果
 
 XTuner 推荐使用基于 LLM-QLoRA / ViT-LoRA 的 LLaVA 架构，其在各个数据集的评测结果如下：