更新了ReadMe

README.md

@@ -214,6 +214,14 @@ streamlit run fast_inference.py
     deepspeed --master_port 29500 --num_gpus=N 3-full_sft.py
     ```
 
+* 记录训练过程
+    ```bash
+    torchrun --nproc_per_node N 1-pretrain.py --use_wandb
+    # and
+    python 1-pretrain.py --use_wandb
+    ```
+    通过添加`--use_wandb`参数，可以记录训练过程，训练完成后，可以在wandb网站上查看训练过程。通过修改`wandb_project`和`wandb_run_name`参数，可以指定项目名称和运行名称。
+
 # 📌 Data sources
 
 - 🤖 分词器：nlp中的Tokenizer类似于词典，将单词从自然语言通过“词典”映射到0,1,36这样的数字，可以理解为数字就代表了单词在“词典”中的页码。

README_en.md

@@ -237,6 +237,13 @@ git clone https://github.com/jingyaogong/minimind.git
     # and
     deepspeed --master_port 29500 --num_gpus=N 3-full_sft.py
     ```
+* Record the training process
+    ```bash
+    torchrun --nproc_per_node N 1-pretrain.py --use_wandb
+    # and
+    python 1-pretrain.py --use_wandb
+    ```
+    By adding the `--use_wandb` parameter, you can record the training process. After training is complete, you can view the training process on the wandb website. You can specify the project name and run name by modifying the `wandb_project` and `wandb_run_name` parameters.
 
 # 📌 Data sources