update qwen1.5 best practice (#520)

README.md

@@ -64,6 +64,7 @@ Users can check the [documentation of SWIFT](docs/source/GetStarted/快速使用
 
 
 ## 🎉 News
+- 🔥2024.03.10: For the end-to-end best practice of fine-tuning to deployment of Qwen1.5-7B-Chat and Qwen1.5-72B-Chat, you can refer to the [Qwen1.5 Full Workflow Best Practice](https://github.com/modelscope/swift/blob/main/docs/source/LLM/Qwen1.5%E5%85%A8%E6%B5%81%E7%A8%8B%E6%9C%80%E4%BD%B3%E5%AE%9E%E8%B7%B5.md).
 - 🔥2024.03.09: Support training and inference of MAMBA series, use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/mamba-1.4b/lora/sft.sh) to begin.
 - 2024.03.09: Support training and inference of AQLM quantized models, use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/llama2_7b_aqlm_2bit_1x16/lora/sft.sh) to begin.
 - 2024.03.06: Support training and inference of AWQ quantized models, use [this Qwen1.5-AWQ script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/qwen1half_7b_chat_awq/lora/sft.sh) to begin, support training and inference of [yi-9b](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/yi_9b/lora_zero3).
@@ -106,7 +107,7 @@ Users can check the [documentation of SWIFT](docs/source/GetStarted/快速使用
 - 2023.12.08: Support [sus-34b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/sus_34b_chat), support yi-6b-200k, yi-34b-200k.
 - 2023.12.07: Support [Multi-Node DDP training](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM%E5%BE%AE%E8%B0%83%E6%96%87%E6%A1%A3.md#%E4%BD%BF%E7%94%A8cli).
 - 2023.12.04: Supported models: zephyr-7b-beta-chat, openbuddy-zephyr-7b-chat. Supported datasets: hc3-zh, hc3-en.
-- 🔥 2023.12.02: [Best Practices for Self-cognition Fine-tuning](https://github.com/modelscope/swift/blob/main/docs/source/LLM/自我认知微调最佳实践.md), **10 minutes for self-cognition fine-tuning for LLM**, creating a LLM that is specific to oneself.
+- 🔥 2023.12.02: [Best Practice for Self-cognition Fine-tuning](https://github.com/modelscope/swift/blob/main/docs/source/LLM/自我认知微调最佳实践.md), **10 minutes for self-cognition fine-tuning for LLM**, creating a LLM that is specific to oneself.
 - 🔥 2023.11.30: Support for training and inference of the **qwen-1_8b**, **qwen-72b**, and **qwen-audio** model series. The corresponding shell scripts can be viewed at [qwen_1_8b_chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_1_8b_chat), [qwen_72b_chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_72b_chat), [qwen_audio_chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_audio_chat).
 - 🔥 2023.11.29: Support the training and inference for **AnimateDiff**
 - 🔥 2023.11.24: Support for **yi-34b-chat**, **codefuse-codellama-34b-chat**: The corresponding shell script can be found in [yi_34b_chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/yi_34b_chat), [codefuse_codellama_34b_chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/codefuse_codellama_34b_chat).
@@ -136,6 +137,9 @@ Users can check the [documentation of SWIFT](docs/source/GetStarted/快速使用
 
 
 ## ✨ LLM Training and Inference
+If you want to learn more about best practices for fine-tuning LLM or tutorials on inference, fine-tuning, quantization, and deployment of LLM, you can refer to the [LLM series documentation](https://github.com/modelscope/swift/blob/main/docs/source/LLM/index.md).
+
+
 ### WEB UI training and inference
 
 After installation, you can use web-ui training/inference like:
@@ -155,20 +159,6 @@ Here is a simple introduction of web-ui:
 
 [![Watch the video](docs/source/cources/resources/20240119160942.jpg)](https://modelscope-open.oss-cn-hangzhou.aliyuncs.com/SWIFT%E8%A7%86%E9%A2%91_%E8%B0%83%E6%95%B4%E5%B0%81%E9%9D%A2.mp4)
 
-### Simple Usage
-
-- **Self-cognition fine-tuning** for large models in **10 minutes**, creating a personalized large model, please refer to [Best Practices for Self-cognition Fine-tuning](https://github.com/modelscope/swift/blob/main/docs/source/LLM/自我认知微调最佳实践.md).
-- Quickly perform **inference** on LLM and build a **Web-UI**, see the [LLM Inference Documentation](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM推理文档.md).
-- Rapidly **fine-tune** and perform inference on LLM, and build a Web-UI, see the [LLM Fine-tuning Documentation](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM微调文档.md).
-- Using **interface** to fine-tuning and perform inference, see the [WEB-UI Documentation](https://github.com/modelscope/swift/blob/main/docs/source/GetStarted/%E7%95%8C%E9%9D%A2%E8%AE%AD%E7%BB%83%E6%8E%A8%E7%90%86.md).
-- **DPO training** supported, see the [DPO Documentation](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM%E4%BA%BA%E7%B1%BB%E5%AF%B9%E9%BD%90%E8%AE%AD%E7%BB%83%E6%96%87%E6%A1%A3.md).
-- Export fine-tuned models, including: merge-lora, AWQ/GPTQ quantization, and push to ModelScope Hub. For more details, please refer to the [LLM Quantization Documentation](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM%E9%87%8F%E5%8C%96%E6%A8%A1%E5%9E%8B.md).
-- Utilize VLLM for **inference acceleration** and **deployment(OpenAI API)**. Please refer to [VLLM Inference Acceleration and Deployment](https://github.com/modelscope/swift/blob/main/docs/source/LLM/VLLM推理加速与部署.md) for more information.
-- View the models and datasets supported by Swift. You can check [supported models and datasets](https://github.com/modelscope/swift/blob/main/docs/source/LLM/支持的模型和数据集.md).
-- Expand and customize models, datasets, and dialogue templates in Swift, see [Customization and Expansion](https://github.com/modelscope/swift/blob/main/docs/source/LLM/自定义与拓展.md).
-- Check command-line parameters for fine-tuning and inference, see [Command-Line parameters](https://github.com/modelscope/swift/blob/main/docs/source/LLM/命令行参数.md).
-- View the training time and training GPU memory comparison under different parameters, you can check [Benchmark](https://github.com/modelscope/swift/blob/main/docs/source/LLM/Benchmark.md).
-
 
 ### Quick Start
 You can test if the environment is installed correctly by running the following code.

README_CN.md

@@ -62,6 +62,7 @@ SWIFT（Scalable lightWeight Infrastructure for Fine-Tuning）是一个可扩展
 用户可以查看 [SWIFT官方文档](docs/source/GetStarted/快速使用.md) 来了解详细信息。
 
 ## 🎉 新闻
+- 🔥2024.03.10: Qwen1.5-7B-Chat与Qwen1.5-72B-Chat从微调到部署[全流程最佳实践](https://github.com/modelscope/swift/blob/main/docs/source/LLM/Qwen1.5%E5%85%A8%E6%B5%81%E7%A8%8B%E6%9C%80%E4%BD%B3%E5%AE%9E%E8%B7%B5.md).
 - 🔥2024.03.09: 支持MAMBA模型的训练和推理, 使用[这个脚本](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/mamba-1.4b/lora/sft.sh)来开始训练！.
 - 2024.03.09: 支持AQLM量化模型的训练和推理, 使用[这个脚本](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/llama2_7b_aqlm_2bit_1x16/lora/sft.sh)开始训练！
 - 2024.03.06: 支持AWQ量化模型的训练和推理, 使用[这个Qwen1.5-AWQ模型脚本](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/qwen1half_7b_chat_awq/lora/sft.sh)开始训练, 并支持[yi-9b](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/yi_9b/lora_zero3)的训练和推理.
@@ -134,6 +135,9 @@ SWIFT（Scalable lightWeight Infrastructure for Fine-Tuning）是一个可扩展
 
 
 ## ✨ 大模型训练推理
+如果想要了解更多大模型微调的最佳实践, 或者对大模型进行推理, 微调, 量化, 部署的教程, 可以查看[LLM系列文档](https://github.com/modelscope/swift/blob/main/docs/source/LLM/index.md).
+
+
 ### WEB UI训练推理
 
 安装SWIFT之后，用如下方式启动界面训练推理：
@@ -155,20 +159,6 @@ swift web-ui
 
 [![Watch the video](docs/source/cources/resources/20240119160942.jpg)](https://modelscope-open.oss-cn-hangzhou.aliyuncs.com/SWIFT%E8%A7%86%E9%A2%91_%E8%B0%83%E6%95%B4%E5%B0%81%E9%9D%A2.mp4)
 
-### 简单使用
-
-- **10分钟**对大模型进行**自我认知微调**, 创建专属于自己的大模型, 可以查看[自我认知微调最佳实践](https://github.com/modelscope/swift/blob/main/docs/source/LLM/自我认知微调最佳实践.md).
-- 快速对LLM进行**推理**, 搭建**Web-UI**, 可以查看[LLM推理文档](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM推理文档.md).
-- 快速对LLM进行**微调**, 推理并搭建Web-UI, 可以查看[LLM微调文档](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM微调文档.md).
-- 使用**界面**方式进行微调和推理, 可以查看[WEB-UI文档](https://github.com/modelscope/swift/blob/main/docs/source/GetStarted/%E7%95%8C%E9%9D%A2%E8%AE%AD%E7%BB%83%E6%8E%A8%E7%90%86.md).
-- 支持**DPO训练**, 可以查看[DPO文档](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM%E4%BA%BA%E7%B1%BB%E5%AF%B9%E9%BD%90%E8%AE%AD%E7%BB%83%E6%96%87%E6%A1%A3.md).
-- 对微调的模型进行导出, 包括: merge-lora, AWQ/GPTQ量化, 推送ModelScope Hub, 可以查看[LLM量化文档](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM%E9%87%8F%E5%8C%96%E6%A8%A1%E5%9E%8B.md).
-- 使用VLLM进行**推理加速**和**部署(OpenAI API)**. 可以查看[VLLM推理加速与部署](https://github.com/modelscope/swift/blob/main/docs/source/LLM/VLLM推理加速与部署.md).
-- 查看swift支持的模型和数据集. 可以查看[支持的模型和数据集](https://github.com/modelscope/swift/blob/main/docs/source/LLM/支持的模型和数据集.md).
-- 对swift中的模型, 数据集, 对话模板进行**拓展**, 可以查看[自定义与拓展](https://github.com/modelscope/swift/blob/main/docs/source/LLM/自定义与拓展.md).
-- 查询微调和推理的命令行参数, 可以查看[命令行参数](https://github.com/modelscope/swift/blob/main/docs/source/LLM/命令行参数.md).
-- 查看不同参数下的训练时间和训练显存对比, 可以查看[Benchmark](https://github.com/modelscope/swift/blob/main/docs/source/LLM/Benchmark.md).
-
 
 ### 快速开始
 你可以通过运行以下代码来测试环境是否安装正确.

docs/source/GetStarted/快速使用.md

@@ -30,7 +30,7 @@ pip install ms-swift -U
 SWIFT库提供了**LLM&AIGC模型的训练推理脚手架**，支持LLaMA、QWen、ChatGLM、Stable Diffusion等多种模型的直接训练和推理，并且集成了SWIFT库提供的tuners，
 开发者可以直接使用。它们的位置在：https://github.com/modelscope/swift/tree/main/examples/pytorch/llm
 
-- LLM训练和推理可以查看: [LLM微调文档](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM微调文档.md)
+- LLM训练和推理可以查看: [LLM系列文档](https://github.com/modelscope/swift/blob/main/docs/source/LLM/index.md)
 - AIGC训练和推理可以查看: [文生图微调文档](https://github.com/modelscope/swift/blob/main/docs/source/AIGC/AnimateDiff微调推理文档.md)
 
 # 使用WEB-UI训练和推理