updated docs and reorg dirs

Signed-off-by: ftgreat <[email protected]>
caowanxia123 · Jun 8, 2023 · f7256aa · f7256aa
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diff --git a/README.md b/README.md
@@ -8,8 +8,15 @@
 --------------------------------------------------------------------------------
 
 
-FlagAI (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model. Our goal is to support training, fine-tuning, and deployment of large-scale models on various downstream tasks with multi-modality. 
+FlagAI (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model. Our goal is to support training, fine-tuning, and deployment of large-scale models on various downstream tasks with multi-modality.
 
+<p align="center">
+Platforms supported
+</p>
+
+****
+                             Tianshu                                      Nvidia            
+****
 
 ## Why should I use FlagAI?
 

diff --git a/README_zh.md b/README_zh.md
@@ -11,7 +11,13 @@
 **FlagAI飞智**是一个快速、易于使用和可扩展的大模型工具包。 我们的目标是支持在多模态的各种下游任务上训练、微调和部署大规模模型。
 <br><br>
 
+<p align="center">
+已支持平台
+</p>
 
+****
+                             天数智芯                                      Nvidia            
+****
 
 ## 为什么你需要 FlagAI?
 

diff --git a/examples/aquila/aquila-code/README_AquilaCode-7B-nv.md b/examples/aquila/aquila-code/README_AquilaCode-7B-nv.md
@@ -120,12 +120,12 @@ with torch.no_grad():
 ```
 
 ### 可监督微调/Supervised Fine-tuning(SFT)
-#### Step 1: 配置模型
+#### Step 1: 配置模型/ Setup Checkpoints
 在`./checkpoints_in`里新建`aquila-7b`目录。将微调后的checkpoint，以及原始`aquila-7b`模型里的其余文件，包括`config.json`, `mergex.txt`, `vocab.json`, `special_tokens_map.json`放进去
 
 Create a new directory named `aquila-7b` inside `./checkpoints_in`. Place the fine-tuned checkpoint and all other files from the original `aquila-7b` model, including `config.json`, `mergex.txt`, `vocab.json`, and `special_tokens_map.json`, into this directory.
 
-#### Step 2: 修改参数
+#### Step 2: 修改参数/Modify Parameters
 * `cd /examples/aquila`
 * 配置`hostfile`文件, 参考[这里](../../../doc_zh/TUTORIAL_8_ENVIRONMENT_SETUP.md#a配置hostfilehostfile-中的v100-1-与sshconfig-对应) ; Configure the `hostfile` file, refer to [here](../../../docs/TUTORIAL_8_ENVIRONMENT_SETUP.md)
 * 配置`bmtrain_mgpu.sh`文件, 将`SCRIPT_FILE`改成`aquila_sft_code.py`; configure the `bmtrain_mgpu.sh` file, change `SCRIPT_FILE` to `aquila_sft_code.py`
@@ -142,11 +142,17 @@ Create a new directory named `aquila-7b` inside `./checkpoints_in`. Place the fi
 | enable_sft_dataset_dir | str   | 可监督微调的数据集目录; Dataset directory of SFT dataset                    |
 | enable_sft_dataset_file | str   | 可监督微调的数据集文件名; Filename of SFT dataset                     |                  |
 
-#### Step 3: 启动微调
+#### Step 3: 启动可监督微调/Start SFT
 ```
 bash dist_trigger_docker.sh hostfile aquila-sft.yaml aquila-7b [实验名]
 ```
+接下来会输出下列信息，注意`NODES_NUM`应该与节点数相等，`LOGFILE`是模型运行的日志文件；The following information will be output. Note that `NODES_NUM` should be equal to the number of nodes, and `LOGFILE` is the log file for the model run.
 
+![Screenshot](../img/info.jpg)
+
+成功训练之前能看到如下信息(具体参数可能不同)； Before successful training, you may see the following information with parameters that may differ:
+
+![Screenshot](../img/info2.jpg)
 
 ## 证书/License
 

diff --git a/examples/aquila/aquila-code/README_AquilaCode-7B-ts.md b/examples/aquila/aquila-code/README_AquilaCode-7B-ts.md
@@ -94,12 +94,12 @@ with torch.no_grad():
 ```
 
 ### 可监督微调/Supervised Fine-tuning(SFT)
-#### Step 1: 配置模型
+#### Step 1: 配置模型/ Setup Checkpoints
 在`./checkpoints_in`里新建`aquila-7b`目录。将微调后的checkpoint，以及原始`aquila-7b`模型里的其余文件，包括`config.json`, `mergex.txt`, `vocab.json`, `special_tokens_map.json`放进去
 
 Create a new directory named `aquila-7b` inside `./checkpoints_in`. Place the fine-tuned checkpoint and all other files from the original `aquila-7b` model, including `config.json`, `mergex.txt`, `vocab.json`, and `special_tokens_map.json`, into this directory.
 
-#### Step 2: 修改参数
+#### Step 2: 修改参数/Modify Parameters
 * `cd /examples/aquila`
 * 配置`hostfile`文件, 参考[这里](../../../doc_zh/TUTORIAL_8_ENVIRONMENT_SETUP.md#a配置hostfilehostfile-中的v100-1-与sshconfig-对应) ; Configure the `hostfile` file, refer to [here](../../../docs/TUTORIAL_8_ENVIRONMENT_SETUP.md)
 * 配置`bmtrain_mgpu.sh`文件, 将`SCRIPT_FILE`改成`aquila_sft_code.py`; configure the `bmtrain_mgpu.sh` file, change `SCRIPT_FILE` to `aquila_sft_code.py`
@@ -117,11 +117,17 @@ Create a new directory named `aquila-7b` inside `./checkpoints_in`. Place the fi
 | enable_sft_dataset_file | str   | 可监督微调的数据集文件名; Filename of SFT dataset                     |                  |
 
 
-#### Step 3: 启动微调
+#### Step 3: 启动可监督微调/Start SFT
 ```
 bash dist_trigger_docker.sh hostfile aquila-sft.yaml aquila-7b [实验名]
 ```
+接下来会输出下列信息，注意`NODES_NUM`应该与节点数相等，`LOGFILE`是模型运行的日志文件；The following information will be output. Note that `NODES_NUM` should be equal to the number of nodes, and `LOGFILE` is the log file for the model run.
 
+![Screenshot](../img/info.jpg)
+
+成功训练之前能看到如下信息(具体参数可能不同)； Before successful training, you may see the following information with parameters that may differ:
+
+![Screenshot](../img/info2.jpg)
 
 ## 证书/License
 

diff --git a/examples/aquila/aquila-pretrain/README_Aquila-33B.md b/examples/aquila/aquila-pretrain/README_Aquila-33B.md
@@ -71,7 +71,13 @@ We used a series of high-quality Chinese and English datasets to train and fine-
 ```
 bash dist_trigger_docker.sh hostfile aquila-pretrain.yaml aquila-30b [实验名]
 ```   
-
+接下来会输出下列信息，注意`NODES_NUM`应该与节点数相等，`LOGFILE`是模型运行的日志文件；The following information will be output. Note that `NODES_NUM` should be equal to the number of nodes, and `LOGFILE` is the log file for the model run.
+
+![Screenshot](../img/info.jpg)
+
+成功训练之前能看到如下信息(具体参数可能不同)； Before successful training, you may see the following information with parameters that may differ:
+
+![Screenshot](../img/info2.jpg)
 
 ### 可监督微调/Supervised Fine-tuning(SFT)
 #### Step 1: 修改参数
@@ -90,10 +96,14 @@ bash dist_trigger_docker.sh hostfile aquila-pretrain.yaml aquila-30b [实验名]
 
 #### Step 2: 启动微调
 ```
-bash dist_trigger_docker.sh hostfile aquila-sft.yaml aquila-30b [实验名]
+bash dist_trigger_docker.sh hostfile aquila-sft.yaml aquila-7b [实验名]
 ```
+接下来会输出下列信息，注意`NODES_NUM`应该与节点数相等，`LOGFILE`是模型运行的日志文件；The following information will be output. Note that `NODES_NUM` should be equal to the number of nodes, and `LOGFILE` is the log file for the model run.
+![Screenshot](../img/info.jpg)
 
+成功训练之前能看到如下信息(具体参数可能不同)； Before successful training, you may see the following information with parameters that may differ:
 
+![Screenshot](../img/info2.jpg)
 ### 推理/Inference
 
 ```python

diff --git a/examples/aquila/aquila-pretrain/README_Aquila-7B.md b/examples/aquila/aquila-pretrain/README_Aquila-7B.md
@@ -68,10 +68,16 @@ The Aquila-7B model was pretrained on Pile，[RedPajama-Data-1T](https://hugging
 ```
 bash dist_trigger_docker.sh hostfile aquila-pretrain.yaml aquila-7b [实验名]
 ```   
-
+ 接下来会输出下列信息，注意`NODES_NUM`应该与节点数相等，`LOGFILE`是模型运行的日志文件；The following information will be output. Note that `NODES_NUM` should be equal to the number of nodes, and `LOGFILE` is the log file for the model run.
+
+![Screenshot](../img/info.jpg)
+
+成功训练之前能看到如下信息(具体参数可能不同)； Before successful training, you may see the following information with parameters that may differ:
+
+![Screenshot](../img/info2.jpg)
 
 ### 可监督微调/Supervised Fine-tuning(SFT)
-#### Step 1: 修改参数
+#### Step 1: 修改参数/Modify Parameters
 * `cd /examples/aquila`
 * 配置`hostfile`文件, 参考[这里](../../../doc_zh/TUTORIAL_8_ENVIRONMENT_SETUP.md#a配置hostfilehostfile-中的v100-1-与sshconfig-对应) ; Configure the `hostfile` file, refer to [here](../../../docs/TUTORIAL_8_ENVIRONMENT_SETUP.md)
 * 配置`bmtrain_mgpu.sh`文件, 将`SCRIPT_FILE`改成`aquila_pretrain.py`; configure the `bmtrain_mgpu.sh` file, change `SCRIPT_FILE` to `aquila_pretrain.py`
@@ -85,12 +91,17 @@ bash dist_trigger_docker.sh hostfile aquila-pretrain.yaml aquila-7b [实验名]
 | warm_up | float   | 初始学习率与原始学习率的比例; The ratio between the initial learning rate and the original learning rate
 | save_interval | int  | 模型保存的间隔，即每训练多少个iteration保存一次模型。当训练时间较长时，保存间隔可以避免因突然中断或出现错误导致训练成果全部丢失; The interval at which the model is saved, i.e., how often the model is saved per epoch during training. When training takes a long time, saving intervals can prevent all training achievements from being lost due to sudden interruptions or errors.                    |
 
-#### Step 2: 启动微调
+#### Step 2: 启动可监督微调/Start SFT
 ```
 bash dist_trigger_docker.sh hostfile aquila-sft.yaml aquila-7b [实验名]
 ```
-接下来会输出下列信息，注意NODES_NUM应该与节点数相等，LOGFILE是模型运行的日志文件
-![Screenshot](img/info.jpg)
+接下来会输出下列信息，注意`NODES_NUM`应该与节点数相等，`LOGFILE`是模型运行的日志文件；The following information will be output. Note that `NODES_NUM` should be equal to the number of nodes, and `LOGFILE` is the log file for the model run.
+
+![Screenshot](../img/info.jpg)
+
+成功训练之前能看到如下信息(具体参数可能不同)； Before successful training, you may see the following information with parameters that may differ:
+
+![Screenshot](../img/info2.jpg)
 
 ### 推理/Inference
 

diff --git a/examples/aquila/aquila-sft/README_AquilaChat-7B.md b/examples/aquila/aquila-sft/README_AquilaChat-7B.md
@@ -172,12 +172,12 @@ with torch.no_grad():
 ```
 
 ### 可监督微调/Supervised Fine-tuning(SFT)
-#### Step 1: 配置模型
+#### Step 1: 配置模型/ Setup Checkpoints
 在`./checkpoints_in`里新建`aquila-7b`目录。将微调后的checkpoint，以及原始`aquila-7b`模型里的其余文件，包括`config.json`, `mergex.txt`, `vocab.json`, `special_tokens_map.json`放进去
 
 Create a new directory named `aquila-7b` inside `./checkpoints_in`. Place the fine-tuned checkpoint and all other files from the original `aquila-7b` model, including `config.json`, `mergex.txt`, `vocab.json`, and `special_tokens_map.json`, into this directory.
 
-#### Step 2: 修改参数
+#### Step 2: 修改参数/ Modify Parameters
 * `cd /examples/aquila`
 * 配置`hostfile`文件, 参考[这里](../../../doc_zh/TUTORIAL_8_ENVIRONMENT_SETUP.md#a配置hostfilehostfile-中的v100-1-与sshconfig-对应) ; Configure the `hostfile` file, refer to [here](../../../docs/TUTORIAL_8_ENVIRONMENT_SETUP.md)
 * 配置`bmtrain_mgpu.sh`文件, 将`SCRIPT_FILE`改成`aquila_sft.py`; configure the `bmtrain_mgpu.sh` file, change `SCRIPT_FILE` to `aquila_sft.py`
@@ -197,11 +197,17 @@ Create a new directory named `aquila-7b` inside `./checkpoints_in`. Place the fi
 
 
 
-#### Step 3: 启动微调
+#### Step 3: 启动可监督微调/Start SFT
 ```
 bash dist_trigger_docker.sh hostfile aquila-sft.yaml aquila-7b [实验名]
 ```
+接下来会输出下列信息，注意`NODES_NUM`应该与节点数相等，`LOGFILE`是模型运行的日志文件；The following information will be output. Note that `NODES_NUM` should be equal to the number of nodes, and `LOGFILE` is the log file for the model run.
 
+![Screenshot](../img/info.jpg)
+
+成功训练之前能看到如下信息(具体参数可能不同)； Before successful training, you may see the following information with parameters that may differ:
+
+![Screenshot](../img/info2.jpg)
 
 
 ## 证书/License