Feat 1028

README.md

@@ -51,9 +51,48 @@ Users can check the [documentation of Swift](docs/source/GetStarted/Introduction
 ## LLM SFT Example
 Press [this link](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm) to view the detail documentation of these examples.
 
+### Basic Usage
+```bash
+git clone https://github.com/modelscope/swift.git
+cd swift
+pip install .[llm]
+```
+
+```python
+# Experimental environment: A10, 3090, A100, ...
+# 16GB GPU memory
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+import torch
+
+from swift.llm import DatasetName, InferArguments, ModelType, SftArguments
+from swift.llm.run import infer_main, sft_main
+
+model_type = ModelType.qwen_7b_chat_int4
+sft_args = SftArguments(
+    model_type=model_type,
+    eval_steps=50,
+    train_dataset_sample=2000,
+    dataset=[DatasetName.leetcode_python_en],
+    output_dir='output',
+    gradient_checkpointing=True)
+best_ckpt_dir = sft_main(sft_args)
+print(f'best_ckpt_dir: {best_ckpt_dir}')
+torch.cuda.empty_cache()
+infer_args = InferArguments(
+    model_type=sft_args.model_type,
+    ckpt_dir=best_ckpt_dir,
+    dataset=sft_args.dataset,
+    stream=True,
+    show_dataset_sample=5)
+infer_main(infer_args)
+```
+
+
 ### Features
 - Supported SFT Methods: [lora](https://arxiv.org/abs/2106.09685), [qlora](https://arxiv.org/abs/2305.14314), full(full parameter fine-tuning)
-- Supported Features: quantization, DDP, model parallelism, gradient checkpointing, gradient accumulation, pushing to modelscope hub, custom datasets, multimodal and agent SFT, mutli-round chat, ...
+- Supported Features: quantization, DDP, model parallelism, gradient checkpointing, pushing to modelscope hub, custom datasets, multimodal and agent SFT, mutli-round chat, ...
 - Supported Models:
   - 🔥 qwen series: [qwen-7b](https://modelscope.cn/models/qwen/Qwen-7B/summary), [qwen-7b-chat](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), [qwen-14b](https://modelscope.cn/models/qwen/Qwen-14B/summary), [qwen-14b-chat](https://modelscope.cn/models/qwen/Qwen-14B-Chat/summary), [qwen-7b-chat-int4](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int4/summary), [qwen-14b-chat-int4](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int4/summary), [qwen-7b-chat-int8](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int8/summary), [qwen-14b-chat-int8](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int8/summary)
   - 🔥 qwen-vl series: [qwen-vl](https://modelscope.cn/models/qwen/Qwen-VL/summary), [qwen-vl-chat](https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary), [qwen-vl-chat-int4](https://modelscope.cn/models/qwen/Qwen-VL-Chat-Int4/summary)
@@ -65,6 +104,7 @@ Press [this link](https://github.com/modelscope/swift/tree/main/examples/pytorch
   - xverse series: [xverse-7b](https://modelscope.cn/models/xverse/XVERSE-7B/summary), [xverse-7b-chat](https://modelscope.cn/models/xverse/XVERSE-7B-Chat/summary), [xverse-13b](https://modelscope.cn/models/xverse/XVERSE-13B/summary), [xverse-13b-chat](https://modelscope.cn/models/xverse/XVERSE-13B-Chat/summary)
   - mistral series: [mistral-7b](https://modelscope.cn/models/AI-ModelScope/Mistral-7B-v0.1/summary), [mistral-7b-chat](https://modelscope.cn/models/AI-ModelScope/Mistral-7B-Instruct-v0.1/summary)
   - ziya series: [ziya2-13b](https://modelscope.cn/models/Fengshenbang/Ziya2-13B-Base/summary), [ziya2-13b-chat](https://modelscope.cn/models/Fengshenbang/Ziya2-13B-Chat/summary)
+  - skywork series: [skywork-13b](https://modelscope.cn/models/skywork/Skywork-13B-base/summary), [skywork-13b-chat](https://modelscope.cn/models/skywork/Skywork-13B-chat/summary)
   - other: [polylm-13b](https://modelscope.cn/models/damo/nlp_polylm_13b_text_generation/summary), [seqgpt-560m](https://modelscope.cn/models/damo/nlp_seqgpt-560m/summary)
 - Supported Datasets:
   - NLP:
@@ -81,8 +121,8 @@ Press [this link](https://github.com/modelscope/swift/tree/main/examples/pytorch
   - Multi-Modal: 🔥[coco-en](https://modelscope.cn/datasets/modelscope/coco_2014_caption/summary)
   - Custom Dataset
 - Supported Templates:
-  - Text Generation: default-generation, chatglm2-generation
-  - Chat: chatml(qwen), baichuan, chatglm2, chatglm3, llama, openbuddy-llama, default, internlm, xverse
+  - Text Generation: default-generation, chatglm-generation
+  - Chat: chatml(qwen), baichuan, chatglm2, chatglm3, llama, openbuddy-llama, default, internlm, xverse, skywork
 
 
 # Installation

README_CN.md

@@ -49,9 +49,48 @@ SWIFT（Scalable lightWeight Infrastructure for Fine-Tuning）是一个可扩展
 ## 大模型微调的例子
 可以[在这里](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm) 查看LLM微调的使用文档。
 
+### 简单使用
+```bash
+git clone https://github.com/modelscope/swift.git
+cd swift
+pip install .[llm]
+```
+
+```python
+# Experimental environment: A10, 3090, A100, ...
+# 16GB GPU memory
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+import torch
+
+from swift.llm import DatasetName, InferArguments, ModelType, SftArguments
+from swift.llm.run import infer_main, sft_main
+
+model_type = ModelType.qwen_7b_chat_int4
+sft_args = SftArguments(
+    model_type=model_type,
+    eval_steps=50,
+    train_dataset_sample=2000,
+    dataset=[DatasetName.leetcode_python_en],
+    output_dir='output',
+    gradient_checkpointing=True)
+best_ckpt_dir = sft_main(sft_args)
+print(f'best_ckpt_dir: {best_ckpt_dir}')
+torch.cuda.empty_cache()
+infer_args = InferArguments(
+    model_type=sft_args.model_type,
+    ckpt_dir=best_ckpt_dir,
+    dataset=sft_args.dataset,
+    stream=True,
+    show_dataset_sample=5)
+infer_main(infer_args)
+```
+
+
 ### 特性
 - 支持的SFT方法: [lora](https://arxiv.org/abs/2106.09685), [qlora](https://arxiv.org/abs/2305.14314), 全参数微调
-- 支持的特性: 模型量化, DDP, 模型并行, gradient checkpointing, 梯度累加, 支持推送ModelScope Hub, 自定义数据集, 多模态和Agent SFT, 多轮对话, ...
+- 支持的特性: 模型量化, DDP, 模型并行, gradient checkpointing, 支持推送ModelScope Hub, 自定义数据集, 多模态和Agent SFT, 多轮对话, ...
 - 支持的模型
   - 🔥 qwen 系列: [qwen-7b](https://modelscope.cn/models/qwen/Qwen-7B/summary), [qwen-7b-chat](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), [qwen-14b](https://modelscope.cn/models/qwen/Qwen-14B/summary), [qwen-14b-chat](https://modelscope.cn/models/qwen/Qwen-14B-Chat/summary), [qwen-7b-chat-int4](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int4/summary), [qwen-14b-chat-int4](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int4/summary), [qwen-7b-chat-int8](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int8/summary), [qwen-14b-chat-int8](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int8/summary)
   - 🔥 qwen-vl 系列: [qwen-vl](https://modelscope.cn/models/qwen/Qwen-VL/summary), [qwen-vl-chat](https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary), [qwen-vl-chat-int4](https://modelscope.cn/models/qwen/Qwen-VL-Chat-Int4/summary)
@@ -63,6 +102,7 @@ SWIFT（Scalable lightWeight Infrastructure for Fine-Tuning）是一个可扩展
   - xverse 系列: [xverse-7b](https://modelscope.cn/models/xverse/XVERSE-7B/summary), [xverse-7b-chat](https://modelscope.cn/models/xverse/XVERSE-7B-Chat/summary), [xverse-13b](https://modelscope.cn/models/xverse/XVERSE-13B/summary), [xverse-13b-chat](https://modelscope.cn/models/xverse/XVERSE-13B-Chat/summary)
   - mistral 系列: [mistral-7b](https://modelscope.cn/models/AI-ModelScope/Mistral-7B-v0.1/summary), [mistral-7b-chat](https://modelscope.cn/models/AI-ModelScope/Mistral-7B-Instruct-v0.1/summary)
   - ziya 系列: [ziya2-13b](https://modelscope.cn/models/Fengshenbang/Ziya2-13B-Base/summary), [ziya2-13b-chat](https://modelscope.cn/models/Fengshenbang/Ziya2-13B-Chat/summary)
+  - skywork 系列: [skywork-13b](https://modelscope.cn/models/skywork/Skywork-13B-base/summary), [skywork-13b-chat](https://modelscope.cn/models/skywork/Skywork-13B-chat/summary)
   - other: [polylm-13b](https://modelscope.cn/models/damo/nlp_polylm_13b_text_generation/summary), [seqgpt-560m](https://modelscope.cn/models/damo/nlp_seqgpt-560m/summary)
 - 支持的数据集:
   - NLP:
@@ -79,8 +119,8 @@ SWIFT（Scalable lightWeight Infrastructure for Fine-Tuning）是一个可扩展
   - 多模态: 🔥[coco-en](https://modelscope.cn/datasets/modelscope/coco_2014_caption/summary)
   - 自定义数据集
 - 支持的对话模板:
-  - 文本生成: default-generation, chatglm2-generation
-  - 对话: chatml(qwen), baichuan, chatglm2, chatglm3, llama, openbuddy-llama, default, internlm, xverse
+  - 文本生成: default-generation, chatglm-generation
+  - 对话: chatml(qwen), baichuan, chatglm2, chatglm3, llama, openbuddy-llama, default, internlm, xverse, skywork
 
 
 # 安装