update doc (#934)

Author: tastelikefeet

README.md

@@ -38,6 +38,8 @@ To facilitate use by users unfamiliar with deep learning, we provide a Gradio we
 
 Additionally, we are expanding capabilities for other modalities. Currently, we support full-parameter training and LoRA training for AnimateDiff.
 
+SWIFT has rich documentations for users, please check [here](https://github.com/modelscope/swift/tree/main/docs/source_en/LLM).
+
 ## 🎉 News
 - 🔥2024.05.13: Support Yi-1.5 series models，use `--model_type yi-1_5-9b-chat` to begin!
 - 2024.05.11: Support for qlora training and quantized inference using [hqq](https://github.com/mobiusml/hqq) and [eetq](https://github.com/NetEase-FuXi/EETQ). For more information, see the [LLM Quantization Documentation](https://github.com/modelscope/swift/tree/main/docs/source_en/LLM/LLM-quantization.md).
@@ -382,6 +384,20 @@ swift sft \
     --deepspeed default-zero3 \
 ```
 
+##### AliYun-DLC multi-node training
+In DLC product, WORLD_SIZE is the node number, RANK is the node index, this is different from the definition of torchrun.
+
+```shell
+NNODES=$WORLD_SIZE \
+NODE_RANK=$RANK \
+swift sft \
+    --model_id_or_path qwen1half-32b-chat \
+    --sft_type full \
+    --dataset blossom-math-zh \
+    --output_dir output \
+    --deepspeed default-zero3
+```
+
 
 ### Inference
 Original model:

README_CN.md

@@ -39,6 +39,8 @@ SWIFT支持近**200种LLM和MLLM**（多模态大模型）的训练、推理、
 
 此外，我们也在拓展其他模态的能力，目前我们支持了AnimateDiff的全参数训练和LoRA训练。
 
+SWIFT具有丰富的文档体系，如有使用问题请请查看[这里](https://github.com/modelscope/swift/tree/main/docs/source/LLM).
+
 ## 🎉 新闻
 - 🔥2024.05.13: 支持Yi-1.5系列模型，使用`--model_type yi-1_5-9b-chat`等开始体验
 - 2024.05.11: 支持使用[hqq](https://github.com/mobiusml/hqq)和[eetq](https://github.com/NetEase-FuXi/EETQ)进行qlora训练和量化推理，可以查看[LLM量化文档](https://github.com/modelscope/swift/tree/main/docs/source/LLM/LLM量化文档.md)
@@ -380,6 +382,21 @@ swift sft \
     --deepspeed default-zero3 \
 ```
 
+##### 阿里云-DLC多机训练
+DLC环境变量中，WORLD_SIZE指代node数量，RANK指代node序号，这一点和torchrun定义不同，需要注意。
+```shell
+NNODES=$WORLD_SIZE \
+NODE_RANK=$RANK \
+swift sft \
+    --model_id_or_path qwen1half-32b-chat \
+    --sft_type full \
+    --dataset blossom-math-zh \
+    --output_dir output \
+    --deepspeed default-zero3
+```
+
+
+
 
 ### 推理
 原始模型:

docs/source/LLM/命令行参数.md

@@ -122,7 +122,7 @@
 - `--train_dataset_mix_ds`: 默认为`['ms-bench']`. 用于防止知识遗忘的通用知识数据集. 该参数已废弃, 请使用`--dataset`进行数据集混合.
 - `--use_loss_scale`: 默认为`False`. 生效时会将Agent的部分字段(Action/Action Input部分)的loss权重加强以强化CoT, 对普通SFT场景没有任何效果.
 - `--custom_register_path`: 默认为`None`. 传入`.py`文件, 用于注册模板、模型和数据集.
-- `--custom_dataset_info`: 默认为`None`, 传入外置dataset_info.json的路径、json字符串或者dict. 用于拓展数据集.
+- `--custom_dataset_info`: 默认为`None`, 传入外置dataset_info.json的路径、json字符串或者dict. 用于拓展数据集. 格式参考: https://github.com/modelscope/swift/blob/main/swift/llm/data/dataset_info.json
 
 
 ### FSDP参数

docs/source_en/LLM/Command-line-parameters.md

@@ -122,7 +122,7 @@
 - `--train_dataset_mix_ds`: Default is `['ms-bench']`. Used for preventing knowledge forgetting, this is the general knowledge dataset. This parameter has been deprecated, please use `--dataset {dataset_name}#{dataset_sample}` to mix datasets.
 - `--use_loss_scale`: Default is `False`. When taking effect, strengthens loss weight of some Agent fields (Action/Action Input part) to enhance CoT, has no effect in regular SFT scenarios.
 - `--custom_register_path`: Default is `None`. Pass in a `.py` file used to register templates, models, and datasets.
-- `--custom_dataset_info`: Default is `None`. Pass in the path to an external `dataset_info.json`, a JSON string, or a dictionary. Used for expanding datasets.
+- `--custom_dataset_info`: Default is `None`. Pass in the path to an external `dataset_info.json`, a JSON string, or a dictionary. Used to register custom datasets. The format example: https://github.com/modelscope/swift/blob/main/swift/llm/data/dataset_info.json
 
 
 ### FSDP Parameters