# 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-swiftgit clone https://github.com/modelscope/swift.gitcd swiftpip 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-Upip 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 doAction: 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 actionObservation: the result of the action... (this Thought/Action/Action Input/Observation can be repeated zero or more times)Thought: I now know the final answerFinal Answer: the final answer to the original input questionBegin!
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 jsonimport 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_prefixfunc_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=1nproc_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_downloadbase_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 namefunction_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 += chunkprint(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_keyexport AMAP_TOKEN=your_api_key

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