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+## swift install
+``` bash
+    git clone https://github.com/modelscope/swift.git
+    cd swift
+    pip install -r requirements.txt
+    pip install -e '.[llm]'
+```
+
+## Swift infer
+### quick start
+1. run the bash code will download the model of MiniCPM-Llama3-V-2_5 and run the inference
+``` shell
+CUDA_VISIBLE_DEVICES=0 swift infer --model_type minicpm-v-v2_5-chat
+```
+
+2. you can also run the code with more arguments below to run the inference:
+``` 
+    model_id_or_path # 可以写huggingface的模型id或者本地模型地址
+    infer_backend ['AUTO', 'vllm', 'pt'] # 后段推理，默认auto
+    dtype ['bf16', 'fp16', 'fp32', 'AUTO'] # 计算精度
+    max_length # 最大长度
+    max_new_tokens: int = 2048 #最多生成多少token
+    do_sample: bool = True # 是否采样
+    temperature: float = 0.3 # 生成时的温度系数
+    top_k: int = 20 
+    top_p: float = 0.7
+    repetition_penalty: float = 1.
+    num_beams: int = 1
+    stop_words: List[str] = None
+    quant_method ['bnb', 'hqq', 'eetq', 'awq', 'gptq', 'aqlm'] # 模型的量化方式
+    quantization_bit [0, 1, 2, 3, 4, 8] 默认是0，代表不使用量化
+```
+3. example:
+``` shell
+    CUDA_VISIBLE_DEVICES=0，1 swift infer \
+    --model_type minicpm-v-v2_5-chat \
+    --model_id_or_path /root/ld/ld_model_pretrain/MiniCPM-Llama3-V-2_5 \
+    --dtype bf16 
+```
+### python code with swift infer
+
+```python
+    import os
+    os.environ['CUDA_VISIBLE_DEVICES'] = '0,1' # 设置显卡数
+
+    from swift.llm import (
+        get_model_tokenizer, get_template, inference, ModelType,
+        get_default_template_type, inference_stream
+    ) # 导入必要模块
+
+    from swift.utils import seed_everything # 设置随机种子
+    import torch
+
+    model_type = ModelType.minicpm_v_v2_5_chat
+    template_type = get_default_template_type(model_type) # 获取模板类型，主要是用于特殊token的构造和图像的处理流程
+    print(f'template_type: {template_type}')
+
+    model, tokenizer = get_model_tokenizer(model_type, torch.bfloat16,
+                                        model_id_or_path='/root/ld/ld_model_pretrain/MiniCPM-Llama3-V-2_5',
+                                        model_kwargs={'device_map': 'auto'}) # 加载模型，并设置模型类型，模型路径，模型参数，设备分配等，计算精度等等
+    model.generation_config.max_new_tokens = 256
+    template = get_template(template_type, tokenizer) # 根据模版类型构造模板
+    seed_everything(42)
+
+    images = ['http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/road.png'] # 图片地址
+    query = '距离各城市多远？'
+    response, history = inference(model, template, query, images=images) # 推理获得结果
+    print(f'query: {query}')
+    print(f'response: {response}')
+
+    # 流式
+    query = '距离最远的城市是哪？'
+    gen = inference_stream(model, template, query, history, images=images) # 调用流式输出接口
+    print_idx = 0
+    print(f'query: {query}\nresponse: ', end='')
+    for response, history in gen:
+        delta = response[print_idx:]
+        print(delta, end='', flush=True)
+        print_idx = len(response)
+    print()
+    print(f'history: {history}')
+```
+
+## Swift train
+1. make the train data like this:
+```jsonl
+{"query": "这张图片描述了什么", "response": "这张图片有一个大熊猫", "images": ["local_image_path"]}
+{"query": "这张图片描述了什么", "response": "这张图片有一个大熊猫", "history": [], "images": ["image_path"]}
+{"query": "竹子好吃么", "response": "看大熊猫的样子挺好吃呢", "history": [["这张图有什么", "这张图片有大熊猫"], ["大熊猫在干嘛", "吃竹子"]], "images": ["image_url"]}
+```
+2. lora turning:
+ the lora target model are k and v weight in llm
+ you should pay attention to the eval_steps,  maybe you should set the eval_steps to a large value, like 200000,beacuase in the eval time , swift will return a memory bug so you should set the eval_steps to a very large value.
+```shell
+    # Experimental environment: A100
+    # 32GB GPU memory
+    CUDA_VISIBLE_DEVICES=0 swift sft \
+        --model_type minicpm-v-v2_5-chat \
+        --dataset coco-en-2-mini \
+```
+3. all parameters finetune:
+when the argument of lora_target_modules is ALL, the model will finetune all the parameters.
+```shell
+CUDA_VISIBLE_DEVICES=0,1 swift sft \
+    --model_type minicpm-v-v2_5-chat \
+    --dataset coco-en-2-mini \
+    --lora_target_modules ALL \
+    --eval_steps 200000
+```
+
+## lora merge and infer
+1. load the lora weight to infer run the follow code:
+```shell
+CUDA_VISIBLE_DEVICES=0 swift infer    \
+ --ckpt_dir /your/lora/save/checkpoint
+```
+2. merge the lora weight to the base model:
+the code will load and merge the lora weight to the base model, save the merge model to the lora save path and load the merge model to infer
+```shell
+CUDA_VISIBLE_DEVICES=0 swift infer \
+    --ckpt_dir your/lora/save/checkpoint \
+    --merge_lora true
+```
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