# 设置pip全局镜像 (加速下载)
pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
# 安装ms-swift
git clone https://github.com/modelscope/swift.git
cd swift
pip install -e .[llm]
# 环境对齐 (通常不需要运行. 如果你运行错误, 可以跑下面的代码, 仓库使用最新环境测试)
pip install -r requirements/framework.txt  -U
pip install -r requirements/llm.txt  -U

Answer the following questions as best you can. You have access to the following APIs:
1. fire_recognition: Call this tool to interact with the fire recognition API. This API is used to recognize whether there is fire in the image. Parameters: [{"name": "image", "description": "The input image to recognize fire", "required": "True"}]
Use the following format:
Thought: you should always think about what to do
Action: the action to take, should be one of the above tools[fire_recognition, fire_alert, call_police, call_fireman]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
输入图片是/tmp/2.jpg，协助判断图片中是否存在着火点
# 工具

# 工具
## 你拥有如下工具：
amap_weather: amap_weather API。获取对应城市的天气数据 输入参数: {"type": "object", "properties": {"location": {"type": "string", "description": "城市/区具体名称，如`北京市海淀区`请描述为`海淀区`"}}, "required": ["location"]} Format the arguments as a JSON object.
## 当你需要调用工具时，请在你的回复中穿插如下的工具调用命令，可以根据需求调用零次或多次：
工具调用
Action: 工具的名称，必须是[amap_weather]之一
Action Input: 工具的输入
Observation: <result>工具返回的结果</result>
Answer: 根据Observation总结本次工具调用返回的结果，如果结果中出现url，请使用如下格式展示出来：![图片](url)
# 指令
你扮演一个天气预报助手，你需要查询相应地区的天气，并调用给你的画图工具绘制一张城市的图。
请注意：你具有图像和视频的展示能力，也具有运行代码的能力，不要在回复中说你做不到。
(。你可以使用工具：[amap_weather])朝阳区天气怎样？

import json
import re
sys_prefix = "\n# 工具\n\n## 你拥有如下工具：\n\n"
def _process_system(text):
    apis_info = []
    api_pattern = r"(?<=\n\d\.)(.*?})(?=])"
    apis = re.findall(api_pattern,text,re.DOTALL)
    sys_prompt = sys_prefix
    func_names = []
    for api in apis:
        func_name = re.search(r'(.*?):', api).group(1).strip()
        func_names.append(func_name)
        api_name = re.search(r'(\S+)\sAPI', api).group(1)
        api_desc = re.search(r"useful for\?\s(.*?)\.",api).group(1)
        sys_prompt += f"{func_name}: {api_name} API。{api_desc}" + "输入参数: {\"type\": \"object\", \"properties\": {"
        paras = re.findall(r"Parameters: \[({.*})",api,re.DOTALL)
        required_paras = []
        for para in paras:
            para_name = re.search(r'"name": "(.*?)"',para).group(1)
            desc = re.search(r'"description": "(.*?)"',para).group(1)
            if re.search(r'"required": "(.*)"',para).group(1).strip().lower() == "true": required_paras.append(para_name)
            sys_prompt += f'"\{para_name}\": {{\"type\": \"string\", \"description\": \"{desc}\"}}' 
        sys_prompt += "},\"required\": " + json.dumps(required_paras) + "} Format the arguments as a JSON object." + "\n\n"
    func_names = json.dumps(func_names)
    sys_prompt += f"## 当你需要调用工具时，请在你的回复中穿插如下的工具调用命令，可以根据需求调用零次或多次：\n\n工具调用\nAction: 工具的名称，必须是{func_names}之一\nAction Input: 工具的输入\nObservation: <result>工具返回的结果</result>\nAnswer: 根据Observation总结本次工具调用返回的结果，如果结果中出现url，请使用如下格式展示出来：![图片](url)\n\n\n# 指令\n\n你扮演AI-Agent，\n你具有下列具体功能：\n下面你将开始扮演\n\n请注意：你具有图像和视频的展示能力，也具有运行代码的能力，不要在回复中说你做不到。\n"
    return sys_prompt
jsonl_file_path = 'ms_agent/train_agent_react.jsonl'
target_file_path = 'new_ms_agent.jsonl'
modified_data = []
with open(jsonl_file_path, 'r', encoding='utf-8') as file:
    for line in file:
        json_obj = json.loads(line)
        system_prompt = json_obj["conversations"][0]["value"]
        json_obj["conversations"][0]["value"] = _process_system(system_prompt)
        modified_data.append(json_obj)
with open(target_file_path, 'w', encoding='utf-8') as file:
    for json_obj in modified_data:
        file.write(json.dumps(json_obj, ensure_ascii=False) + '\n')

# Experimental environment: A100
cd examples/pytorch/llm
# 如果使用1张卡则配置nproc_per_node=1
nproc_per_node=8
export PYTHONPATH=../../..
# 时间比较久，8*A100需要 2+小时，nohup运行
nohup torchrun \
    --nproc_per_node=$nproc_per_node \
    --master_port 29500 \
    llm_sft.py \
    --model_id_or_path qwen/Qwen-7B-Chat \
    --model_revision master \
    --sft_type lora \
    --tuner_backend swift \
    --dtype AUTO \
    --output_dir output \
    --custom_train_dataset_path ms_agent_for_agentfabric/new_ms_agent.jsonl ms_agent_for_agentfabric/addition.jsonl
    --train_dataset_mix_ratio 2.0 \
    --train_dataset_sample -1 \
    --num_train_epochs 2 \
    --max_length 2048 \
    --check_dataset_strategy warning \
    --lora_rank 8 \
    --lora_alpha 32 \
    --lora_dropout_p 0.05 \
    --lora_target_modules ALL \
    --self_cognition_sample 3000 \
    --model_name 卡卡罗特 \
    --model_author 陶白白 \
    --gradient_checkpointing true \
    --batch_size 2 \
    --weight_decay 0.01 \
    --learning_rate 5e-5 \
    --gradient_accumulation_steps $(expr 1 / $nproc_per_node) \
    --max_grad_norm 0.5 \
    --warmup_ratio 0.03 \
    --eval_steps 100 \
    --save_steps 100 \
    --save_total_limit 2 \
    --logging_steps 10 &

[INFO:swift] best_model_checkpoint: /home/workspace/swift/examples/pytorch/llm/output/qwen-7b-chat/v0-20240314-211944/checkpoint-2828
[INFO:swift] images_dir: /home/workspace/swift/examples/pytorch/llm/output/qwen-7b-chat/v0-20240314-211944/images
[INFO:swift] End time of running main: 2024-03-14 23:33:54.658745

python tools/merge_lora_weights_to_model.py --model_id_or_path /dir/to/your/base/model --model_revision master --ckpt_dir /dir/to/your/lora/model

from modelscope import snapshot_download
base_model_path = snapshot_download('qwen/Qwen-7B-Chat')
print(base_model_path)

CUDA_VISIBLE_DEVICES=0 swift export \
    --ckpt_dir '/path/to/qwen-7b-chat/vx-xxx/checkpoint-xxx' --merge_lora true

[INFO:swift] Saving merged weights...
[INFO:swift] Successfully merged LoRA and saved in /home/workspace/swift/examples/pytorch/llm/output/qwen-7b-chat/v0-20240314-211944/checkpoint-2828-merged.
[INFO:swift] End time of running main: 2024-03-18 10:34:54.307471

nohup python -m vllm.entrypoints.openai.api_server --model /dir/to/your/model-merged --trust-remote-code &

INFO:     Started server process [531583]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

curl http://localhost:8000/v1/completions -H "Content-Type: application/json" -d '{"model": "/dir/to/your/model-merged", "prompt": "San Francisco is a", "max_tokens": 7, "temperature": 0}'

CUDA_VISIBLE_DEVICES=0 swift deploy --ckpt_dir /path/to/qwen-7b-chat/vx-xxx/checkpoint-xxxx-merged

from modelscope_agent.agents.role_play import RolePlay  # NOQA
def test_weather_role():
    role_template = '你扮演一个天气预报助手，你需要查询相应地区的天气，并调用给你的画图工具绘制一张城市的图。'
    llm_config =  {
        "model_server": "openai",
        "model": "/dir/to/your/model-merged",
        "api_base": "http://localhost:8000/v1",
        "is_chat": True,
        "is_function_call": False,
        "support_stream": False
    }
    #llm_config = {"model": "qwen-max", "model_server": "dashscope"}
    # input tool name
    function_list = ['amap_weather']
    bot = RolePlay(
        function_list=function_list, llm=llm_config, instruction=role_template)
    response = bot.run('朝阳区天气怎样？')
    text = ''
    for chunk in response:
        text += chunk
    print(text)
    assert isinstance(text, str)
test_weather_role()

cd modelscope-agent/apps/agentfabric

"my-qwen-7b-chat": {
        "type": "openai",
        "model": "/dir/to/your/model-merged",
        "api_base": "http://localhost:8000/v1",
        "is_chat": true,
        "is_function_call": false,
        "support_stream": false
    }

export DASHSCOPE_API_KEY=your_api_key
export AMAP_TOKEN=your_api_key

GRADIO_SERVER_NAME=0.0.0.0 PYTHONPATH=../../  python app.py