update readme (#3011)

Author: Jintao-Huang

README.md

@@ -55,7 +55,7 @@ You can contact us and communicate with us by adding our group:
 
 
 ## 📝 Introduction
-🍲 ms-swift is an official framework provided by the ModelScope community for fine-tuning and deploying large language models and multi-modal large models. It currently supports the training (pre-training, fine-tuning, human alignment), inference, evaluation, quantization, and deployment of 450+ large models and 150+ multi-modal large models. These large language models (LLMs) include models such as Qwen2.5, InternLM3, GLM4, Llama3.3, Mistral, DeepSeek3, Yi1.5, TeleChat2, Baichuan2, and Gemma2. The multi-modal LLMs include models such as Qwen2.5-VL, Qwen2-Audio, Llama3.2-Vision, Llava, InternVL2.5, MiniCPM-V-2.6, GLM4v, Xcomposer2.5, Yi-VL, DeepSeek-VL2, Phi3.5-Vision, and GOT-OCR2.
+🍲 ms-swift is an official framework provided by the ModelScope community for fine-tuning and deploying large language models and multi-modal large models. It currently supports the training (pre-training, fine-tuning, human alignment), inference, evaluation, quantization, and deployment of 450+ large models and 150+ multi-modal large models. These large language models (LLMs) include models such as Qwen2.5, InternLM3, GLM4, Llama3.3, Mistral, DeepSeek-R1, Yi1.5, TeleChat2, Baichuan2, and Gemma2. The multi-modal LLMs include models such as Qwen2.5-VL, Qwen2-Audio, Llama3.2-Vision, Llava, InternVL2.5, MiniCPM-V-2.6, GLM4v, Xcomposer2.5, Yi-VL, DeepSeek-VL2, Phi3.5-Vision, and GOT-OCR2.
 
 🍔 In addition, ms-swift gathers the latest training technologies, including LoRA, QLoRA, Llama-Pro, LongLoRA, GaLore, Q-GaLore, LoRA+, LISA, DoRA, FourierFt, ReFT, UnSloth, and Liger. ms-swift supports acceleration of inference, evaluation, and deployment modules using vLLM and LMDeploy, and supports the quantization of large models and multi-modal large models using technologies such as GPTQ, AWQ, and BNB. To help researchers and developers fine-tune and apply large models more easily, ms-swift also provides a Gradio-based Web-UI interface and a wealth of best practices.
 
@@ -103,6 +103,24 @@ cd ms-swift
 pip install -e .
 ```
 
+Running Environment:
+
+|              | Range                | Recommended | Notes                                     |
+| ------------ | -------------------- | ----------- | ----------------------------------------- |
+| python       | >=3.8                | 3.10        |                                           |
+| cuda         |                      | cuda12      | No need to install if using CPU, NPU, MPS |
+| torch        | >=2.0                |             |                                           |
+| transformers | >=4.33               | 4.48.1      |                                           |
+| modelscope   | >=1.19               |             |                                           |
+| peft         | >=0.11.0,<0.15.0     |             |                                           |
+| trl          | >=0.13,<0.15         | 0.14.0      | RLHF                                      |
+| vllm         | >=0.5.1              | 0.6.5       | Inference/Deployment/Evaluation           |
+| lmdeploy     | lmdeploy>=0.5,<0.6.5 | 0.6.4       | Inference/Deployment/Evaluation           |
+| deepspeed    |                      | 0.14.5      | Training                                  |
+
+For more optional dependencies, you can refer to [here](https://github.com/modelscope/ms-swift/blob/main/requirements/install_all.sh).
+
+
 ## 🚀 Quick Start
 
 10 minutes of self-cognition fine-tuning of Qwen2.5-7B-Instruct on a single 3090 GPU:
@@ -174,7 +192,7 @@ SWIFT_UI_LANG=en swift web-ui
 
 ### Using Python
 
-ms-swift also supports training and inference using Python. Below is pseudocode for training and inference. For more details, you can refer to [here](https://github.com/modelscope/ms-swift/tree/main/examples/notebook).
+ms-swift also supports training and inference using Python. Below is pseudocode for training and inference. For more details, you can refer to [here](https://github.com/modelscope/ms-swift/blob/main/examples/notebook/qwen2_5-self-cognition/self-cognition-sft.ipynb).
 
 Training:
 
@@ -213,17 +231,37 @@ print(f'response: {resp_list[0].choices[0].message.content}')
 ```
 
 ## ✨ Usage
-Here is the simplest example of training to deployment using ms-swift. For more details, you can check the [examples](https://github.com/modelscope/ms-swift/tree/main/examples).
+Here is a minimal example of training to deployment using ms-swift. For more details, you can check the [examples](https://github.com/modelscope/ms-swift/tree/main/examples).
+
+- If you want to use other models or datasets (including multimodal models and datasets), you only need to modify `--model` to specify the corresponding model's ID or path, and modify `--dataset` to specify the corresponding dataset's ID or path.
+- By default, ModelScope is used for downloading models and datasets. If you want to use HuggingFace, simply specify `--use_hf true`.
 
 |   Useful Links |
 | ------ |
-|   [Command Line Parameters](https://swift.readthedocs.io/en/latest/Instruction/Command-line-parameters.html)   |
+|   [🔥Command Line Parameters](https://swift.readthedocs.io/en/latest/Instruction/Command-line-parameters.html)   |
 |   [Supported Models and Datasets](https://swift.readthedocs.io/en/latest/Instruction/Supported-models-and-datasets.html)   |
-|   [Custom Models](https://swift.readthedocs.io/en/latest/Customization/Custom-model.html), [Custom Datasets](https://swift.readthedocs.io/en/latest/Customization/Custom-dataset.html)   |
+|   [Custom Models](https://swift.readthedocs.io/en/latest/Customization/Custom-model.html), [🔥Custom Datasets](https://swift.readthedocs.io/en/latest/Customization/Custom-dataset.html)   |
 |   [LLM Tutorial](https://github.com/modelscope/modelscope-classroom/tree/main/LLM-tutorial)   |
 
 ### Training
 
+Supported Training Methods:
+
+| Method                             | Full-Parameter                                               | LoRA                                                         | QLoRA                                                        | Deepspeed                                                    | Multi-modal                                                  |
+| ---------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| Pre-training                       | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/pretrain/train.sh) | ✅                                                            | ✅                                                            | ✅                                                            | ✅                                                            |
+| Instruction Supervised Fine-tuning | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/full/train.sh) | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/lora_sft.sh) | [✅](https://github.com/modelscope/ms-swift/tree/main/examples/train/qlora) | [✅](https://github.com/modelscope/ms-swift/tree/main/examples/train/multi-gpu/deepspeed) | [✅](https://github.com/modelscope/ms-swift/tree/main/examples/train/multimodal) |
+| DPO Training                       | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/dpo.sh) | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/dpo.sh) | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/multimodal/rlhf/dpo.sh) |
+| Reward Model Training              | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/rm.sh) | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/rm.sh) | ✅                                                            |
+| PPO Training                       | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/ppo.sh) | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/ppo.sh) | ❌                                                            |
+| KTO Training                       | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/kto.sh) | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/kto.sh) | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/multimodal/rlhf/kto.sh) |
+| CPO Training                       | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/cpo.sh) | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/cpo.sh) | ✅                                                            |
+| SimPO Training                     | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/simpo.sh) | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/simpo.sh) | ✅                                                            |
+| ORPO Training                      | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/orpo.sh) | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/orpo.sh) | ✅                                                            |
+| Classification Model Training      | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/seq_cls/qwen2_5/sft.sh) | ✅                                                            | ✅                                                            | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/seq_cls/qwen2_vl/sft.sh) |
+
+
+
 Pre-training:
 ```shell
 # 8*A100
@@ -322,6 +360,15 @@ CUDA_VISIBLE_DEVICES=0 swift export \
     --output_dir Qwen2.5-7B-Instruct-AWQ
 ```
 
+### Push Model
+```shell
+CUDA_VISIBLE_DEVICES=0 swift export \
+    --model <model-path> \
+    --push_to_hub true \
+    --hub_model_id '<model-id>' \
+    --hub_token '<sdk-token>'
+```
+
 ## 🏛 License
 
 This framework is licensed under the [Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE). For models and datasets, please refer to the original resource page and follow the corresponding License.