update readme

README.md

@@ -130,16 +130,16 @@ CPU: Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz
 
 # 📌 Quick Start Train
 
-* 0.环境安装
-  ```bash
-  pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
-  ```
-
-* 1.克隆项目代码
+* 0.克隆项目代码
     ```text
     git clone https://github.com/jingyaogong/minimind-v
     ```
 
+* 1.环境安装
+  ```bash
+  pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+  ```
+
 * 2.下载 `clip-vit-base-patch32` 模型，放到 `./model/clip_model` 目录下：
     ```bash
     cd ./model/clip_model & git clone https://hf-mirror.com/openai/clip-vit-base-patch32

README_en.md

@@ -97,7 +97,7 @@ Environment: python 3.9 + Torch 2.1.2 + DDP single-machine multi-GPU training
 * CUDA == 12.2
 * [requirements.txt](./requirements.txt)
 
-# 📌 Quick Test
+# 📌 Quick Start Test
 
 * 0. Clone the project
     ```bash
@@ -128,22 +128,22 @@ Environment: python 3.9 + Torch 2.1.2 + DDP single-machine multi-GPU training
 
 # 📌 Quick Start Train
 
-* 0. Environment setup
-  ```bash
-  pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
-  ```
-
-* 1. Clone the project code
+* 0.Clone the project code
     ```text
     git clone https://github.com/jingyaogong/minimind-v
     ```
 
-* 2. Download the `clip-vit-base-patch32` model and place it in the `./model/clip_model` directory:
+* 1.Environment setup
+  ```bash
+  pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+  ```
+
+* 2.Download the `clip-vit-base-patch32` model and place it in the `./model/clip_model` directory:
     ```bash
     cd ./model/clip_model & git clone https://hf-mirror.com/openai/clip-vit-base-patch32
     ```
   
-* 3. If you want to train it yourself
+* 3.If you want to train it yourself
 
     * 3.1 Download all contents of the dataset ([Baidu Netdisk](https://pan.baidu.com/s/1Nz36OBBvVBGEx-PwIb7ofg?pwd=6666) or [HuggingFace](https://hf-mirror.com/datasets/jingyaogong/minimind-v_dataset)) to the `./dataset` directory, and unzip `pretrain_images.zip` and `sft_images.zip`
     * 3.2 Adjust the model parameters in `./model/LMConfig.py`
@@ -152,7 +152,7 @@ Environment: python 3.9 + Torch 2.1.2 + DDP single-machine multi-GPU training
     * 3.4 Execute `python 1-pretrain_vlm.py` for pre-training, obtaining `*_vlm_pretrain.pth` as the output weights
     * 3.5 Execute `python 2-sft_vlm.py` for instruction fine-tuning, obtaining `*_vlm_sft.pth` as the output weights for fine-tuning
 
-* 4. Test the inference effect of the self-trained model
+* 4.Test the inference effect of the self-trained model
     * Ensure that the used, completed training parameter weights `*.pth` files are located in the `./out/` directory
     * You can also directly download the [completed model weight files](https://pan.baidu.com/s/1LE1SPoPYGS7VNtT1tpf7DA?pwd=6666) and use the `*.pth` weight files I have trained
        ```text