LLM 大模型学习必知必会系列(九)：Agent 微调最佳实践，用消费级显卡训练属于自己的 Agent！
SWIFT 支持了开源模型，尤其是中小型模型（7B、14B 等）对 Agent 场景的训练，并将loss-scale技术应用到 agent 训练中，使中小模型 API Call 能力更稳定，并支持使用单张商业级显卡进行 Agent 推理和部署，可以直接在生产场景中全链路闭环落地使用。
1.环境安装
#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
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2.数据准备
为训练 Agent 能力，魔搭官方提供了两个开源数据集：
该数据集数据格式如下：
{ "id": "MS_Agent_Bench_126374", "conversations": [{ "from": "system", "value": "Answer the following questions as best you can. You have access to the following APIs:\n1. hm_recipe_recommend: Call this tool to interact with the hmreciperecommend API. What is the hmreciperecommend API useful for? . Parameters: [{\"name\": \"keywords_dict\", \"description\": \"盒马推荐菜谱关键词字典。\", \"required\": \"True\"}]\n\n2. hm_product_marketing: Call this tool to interact with the hmproductmarketing API. What is the hmproductmarketing API useful for? . Parameters: [{\"name\": \"sku_code_list\", \"description\": \"盒马商品的编码列表\", \"required\": \"True\"}]\n\n3. hm_product_info: Call this tool to interact with the hmproductinfo API. What is the hmproductinfo API useful for? . Parameters: [{\"name\": \"sku_code_list\", \"description\": \"盒马商品的编码列表\", \"required\": \"True\"}, {\"name\": \"sku_name_list\", \"description\": \"盒马商品的名称列表\", \"required\": \"True\"}, {\"name\": \"property_list\", \"description\": \"盒马商品的属性列表\", \"required\": \"True\"}]\n\n4. hm_product_recommend: Call this tool to interact with the hmproductrecommend API. What is the hmproductrecommend API useful for? . Parameters: [{\"name\": \"keywords_dict\", \"description\": \"盒马推荐商品关键词字典。\", \"required\": \"True\"}]\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of the above tools[hm_recipe_recommend, hm_product_marketing, hm_product_info, hm_product_recommend]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can be repeated zero or more times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\nBegin!" }, { "from": "user", "value": "你好，请问你们有哪些手机可以推荐？" }, { "from": "assistant", "value": "Action: hm_product_recommend\nAction Input: {'keywords_dict': {'商品类目': '手机'}}\nObservation: [{'sku_code': '10518244314', 'product_name': '小米11 Ultra 5G手机 256GB', 'bussiness_type': '标品', 'product_price': '5999', 'product_description': '拥有120Hz高刷新率和骁龙888处理器，还配备了高品质相机系统'}, {'sku_code': '10222726664', 'product_name': 'vivo X60 Pro 5G手机 256GB', 'bussiness_type': '标品', 'product_price': '4498', 'product_description': '搭载Exynos 1080处理器与ZEISS联合调校的相机系统，拍照实力十足'}, {'sku_code': '18227073452', 'product_name': '华为畅享 20 Pro 5G手机 128GB', 'bussiness_type': '标品', 'product_price': '2699', 'product_description': '拥有4000mAh的电池容量和优秀的相机而价格非常亲民'}]\nThought: I now know the final answer\nFinal Answer: 您好，我们有以下几款手机供您选择：1.小米11 Ultra 5G手机 256GB：价格为5999元，拥有120Hz高刷新率和骁龙888处理器，还配备了高品质相机系统；2. vivo X60 Pro 5G手机 256GB：售价为4498元，搭载Exynos 1080处理器与ZEISS联合调校的相机系统，拍照实力十足；3. 华为畅享 20 Pro 5G手机 128GB：价格亲民，只需2699元即可拥有优秀的相机和4000mAh的电池容量。这些手机都具有不同的特点和功能，您可以根据自己的需求进行选择。如果您有其他问题或需求，请随时告诉我。" }, { "from": "user", "value": "我想买一款拍照好的手机，有推荐吗？" }, { "from": "assistant", "value": "Action: hm_product_recommend\nAction Input: {'keywords_dict': {'商品类目': '手机', '适用场景': '拍照'}}\nObservation: [{'sku_code': '15956662254', 'product_name': '小米11 Ultra 5G手机 256GB', 'bussiness_type': '标品', 'product_price': '5999', 'product_description': '拥有120Hz高刷新率和骁龙888处理器，还配备了高品质相机系统'}, {'sku_code': '19424216918', 'product_name': 'vivo X60 Pro 5G手机 256GB', 'bussiness_type': '标品', 'product_price': '4498', 'product_description': '搭载Exynos 1080处理器与ZEISS联合调校的相机系统，拍照实力十足'}, {'sku_code': '14347982715', 'product_name': 'OPPO Ace2 5G手机 256GB', 'bussiness_type': '标品', 'product_price': '3999', 'product_description': '拍照实力十足，支持65W超级闪充'}]\nThought: I now know the final answer\nFinal Answer: 如果您想要一款拍照表现出色的手机，我为您推荐以下几款产品：1.小米11 Ultra 5G手机 256GB：价格为5999元，配备了高品质相机系统，可以拍摄出色的照片；2. vivo X60 Pro 5G手机 256GB：售价为4498元，相机系统由ZEISS联合调校，拍照实力十足；3. OPPO Ace2 5G手机 256GB：价格为3999元，不仅拍照实力十足，还支持65W超级闪充，让你的手机充电更加便捷。这些手机都有不同的特点和功能，您可以根据自己的需求进行选择。如果您还有其他问题或需求，请随时告诉我。" }, { "from": "user", "value": "小米11 Ultra拍照性能怎么样？" }, { "from": "assistant", "value": "Action: hm_product_info\nAction Input: {'sku_code_list': ['15956662254'], 'sku_name_list': ['小米11 Ultra 5G手机 256GB'], 'property_list': [['拍照性能']]}\nObservation: [{'sku_code': '15956662254', 'property': {'拍照性能': '小米11 Ultra 5G手机配备了1亿像素的主摄像头，还有120X超长焦电影摄像头，以及20MP超广角摄像头等组合，拍照性能非常出色。'}}]\nThought: I now know the final answer\nFinal Answer: 小米11 Ultra 5G手机配备了1亿像素的主摄像头，还有120X超长焦电影摄像头，以及20MP超广角摄像头等组合，拍照性能非常出色。如果您还有其他问题或需求，请随时告诉我。" }]}
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Agent 数据集的 system 字段具体格式如下（将\"字符转换为"字符, \n 转换为换行）：
Answer the following questions as best you can. You have access to the following APIs:1. hm_recipe_recommend: Call this tool to interact with the hmreciperecommend API. What is the hmreciperecommend API useful for? . Parameters: [{"name": "keywords_dict", "description": "盒马推荐菜谱关键词字典。", "required": "True"}]
2. hm_product_marketing: Call this tool to interact with the hmproductmarketing API. What is the hmproductmarketing API useful for? . Parameters: [{"name": "sku_code_list", "description": "盒马商品的编码列表", "required": "True"}]
3. hm_product_info: Call this tool to interact with the hmproductinfo API. What is the hmproductinfo API useful for? . Parameters: [{"name": "sku_code_list", "description": "盒马商品的编码列表", "required": "True"}, {"name": "sku_name_list", "description": "盒马商品的名称列表", "required": "True"}, {"name": "property_list", "description": "盒马商品的属性列表", "required": "True"}]
4. hm_product_recommend: Call this tool to interact with the hmproductrecommend API. What is the hmproductrecommend API useful for? . Parameters: [{"name": "keywords_dict", "description": "盒马推荐商品关键词字典。", "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[hm_recipe_recommend, hm_product_marketing, hm_product_info, hm_product_recommend]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!
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API 格式:
Answer the following questions as best you can. You have access to the following APIs:序号: API名称: API作用 API参数
...
Use the following format:
Thought: you should always think about what to doAction: the action to take, should be one of the above tools[API名称列表]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!
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Agent 数据集调用 API 的 response 的结构如下：
Action: hm_product_recommendAction Input: {'keywords_dict': {'商品类目': '手机', '适用场景': '拍照'}}Observation: [{'sku_code': '15956662254', 'product_name': '小米11 Ultra 5G手机 256GB', 'bussiness_type': '标品', 'product_price': '5999', 'product_description': '拥有120Hz高刷新率和骁龙888处理器，还配备了高品质相机系统'}, {'sku_code': '19424216918', 'product_name': 'vivo X60 Pro 5G手机 256GB', 'bussiness_type': '标品', 'product_price': '4498', 'product_description': '搭载Exynos 1080处理器与ZEISS联合调校的相机系统，拍照实力十足'}, {'sku_code': '14347982715', 'product_name': 'OPPO Ace2 5G手机 256GB', 'bussiness_type': '标品', 'product_price': '3999', 'product_description': '拍照实力十足，支持65W超级闪充'}]Thought: I now know the final answerFinal Answer: 如果您想要一款拍照表现出色的手机，我为您推荐以下几款产品：1.小米11 Ultra 5G手机 256GB：价格为5999元，配备了高品质相机系统，可以拍摄出色的照片；2. vivo X60 Pro 5G手机 256GB：售价为4498元，相机系统由ZEISS联合调校，拍照实力十足；3. OPPO Ace2 5G手机 256GB：价格为3999元，不仅拍照实力十足，还支持65W超级闪充，让你的手机充电更加便捷。这些手机都有不同的特点和功能，您可以根据自己的需求进行选择。如果您还有其他问题或需求，请随时告诉我。
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3.微调
在 Agent 训练中，为了避免训练后造成严重知识遗忘，我们的数据配比为ms-agent:ms-bench数据集 1 比 2，其中 ms_agent 共 30000 条，随机抽样 ms_bench 数据集 60000 条，同时为了改变模型认知，增加自我认知数据 3000 条。
我们也支持使用自己的 Agent 数据集。数据集格式需要符合自定义数据集的要求。更具体地，Agent 的 response/system 应该符合上述的 Action/Action Input/Observation 格式。
我们将 MLP 和 Embedder 加入了 lora_target_modules. 你可以通过指定--lora_target_modules ALL在所有的 linear 层(包括 qkvo 以及 mlp 和 embedder)加 lora. 这通常是效果最好的.
微调使用了 qwen-7b-chat 模型，超参数如下：
运行命令和其他超参数如下:
#Experimental environment: 8GPUnproc_per_node=8
PYTHONPATH=../../.. \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 peft \ --dtype AUTO \ --output_dir output \ --dataset ms-agent \ --train_dataset_mix_ratio 2.0 \ --train_dataset_sample -1 \ --num_train_epochs 2 \ --max_length 1500 \ --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 0.1 \ --learning_rate 5e-5 \ --gradient_accumulation_steps $(expr 32 / $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
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在官方实验中，训练过程使用了 8GPU 硬件环境，训练时长 3 小时。
[!NOTE]
该训练使用消费级单显卡也可以运行（对应占用显存 22G），用户将 DDP 命令改为单卡命令即可
LoRA 训练的遗忘问题并不严重，可以适当调低 ms-bench 数据集的比例，提高训练速度
4.推理
我们针对通用知识和 Agent 进行评测。下面列出了一个简单的评测结果。
4.1 原始模型
西湖醋鱼怎么做
新冠和普通感冒有什么区别
4.2 Agent 能力
我们使用一个火焰报警场景作为测试用例：
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"}]
2. fire_alert: Call this tool to interact with the fire alert API. This API will start an alert to warn the building's administraters. Parameters: []
3. call_police: Call this tool to interact with the police calling API. This API will call 110 to catch the thief. Parameters: []
4. call_fireman: Call this tool to interact with the fireman calling API. This API will call 119 to extinguish the fire. Parameters: []
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!
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可以看到，人工输入 Observation 后模型答案并不正确。
4.3 训练后
西湖醋鱼怎么做
新冠和普通感冒有什么区别
可以看到，训练后模型可以正确调用 API 并给出最终答案。
目前命令行的 Agent 推理支持需要指定--eval_human true，因为该参数为 false 的时候会读取数据集内容，此时无法手动传入Observation:后面的 API 调用结果。
# 使用训练后的模型swift infer --ckpt_dir output/qwen-7b-chat/vx-xxx/checkpoint-xxx --eval_human true --stop_words Observation: --infer_backend pt# 也可以使用原始模型，如qwn-7b-chat或chatglm3-6b-32k等运行agent# swift infer --model_type qwen-7b-chat --eval_human true --stop_words Observation: --infer_backend pt# swift infer --model_type chatglm3-6b-32k --eval_human true --stop_words Observation: --infer_backend pt
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运行命令后，改变 system 字段：
# 单行system<<< reset-system<<< Answer the following questions as best you can. You have access to the following APIs:\n1. 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"}]\n\n2. fire_alert: Call this tool to interact with the fire alert API. This API will start an alert to warn the building's administraters. Parameters: []\n\n3. call_police: Call this tool to interact with the police calling API. This API will call 110 to catch the thief. Parameters: []\n\n4. call_fireman: Call this tool to interact with the fireman calling API. This API will call 119 to extinguish the fire. Parameters: []\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of the above tools[fire_recognition, fire_alert, call_police, call_fireman]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can be repeated zero or more times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\nBegin!
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如果需要以多行方式输入，可以用下面的命令(多行信息以 #号结束)：
# 多行system<<< multi-line<<<[M] reset-system#<<<[MS] 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"}]
2. fire_alert: Call this tool to interact with the fire alert API. This API will start an alert to warn the building's administraters. Parameters: []
3. call_police: Call this tool to interact with the police calling API. This API will call 110 to catch the thief. Parameters: []
4. call_fireman: Call this tool to interact with the fireman calling API. This API will call 119 to extinguish the fire. Parameters: []
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!#
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下面就可以进行 Agent 问答(注意如果使用多行模式输入行尾额外增加 #号)：
<<< 输入图片是/tmp/1.jpg，协助判断图片中是否存在着火点Thought: I need to use the fire\_recognition API to analyze the input image and determine if there are any signs of fire.
Action: Use the fire\_recognition API to analyze the input image.
Action Input: /tmp/1.jpg
Observation:<<< [{'coordinate': [101.1, 200.9], 'on_fire': True}]Thought: The fire\_recognition API has returned a result indicating that there is fire in the input image.
Final Answer: There is fire in the input image.
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可以看到，模型已经返回了 API 调用的结果分析。用户可以继续问问题进行多轮 Agent 场景。也可以指定--infer_backend vllm和--stream true来使用 vllm 和流式推理。
5.在部署中使用 Agent
由于部署不支持 history 管理，因此 agent 的 API 调用结果拼接需要用户自行进行，下面给出一个 OpenAI 格式可运行的代码范例。
服务端：
# 使用训练后的模型swift deploy --ckpt_dir output/qwen-7b-chat/vx-xxx/checkpoint-xxx --stop_words Observation:# 也可以使用原始模型，如qwen-7b-chat或chatglm3-6b-32k等运行agent# swift deploy --model_type qwn-7b-chat --stop_words Observation:# swift deploy --model_type chatglm3-6b-32k --stop_words Observation:
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客户端：
from openai import OpenAIclient = OpenAI( api_key='EMPTY', base_url='http://localhost:8000/v1',)model_type = client.models.list().data[0].idprint(f'model_type: {model_type}')
system = """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\"}]
2. fire_alert: Call this tool to interact with the fire alert API. This API will start an alert to warn the building's administraters. Parameters: []
3. call_police: Call this tool to interact with the police calling API. This API will call 110 to catch the thief. Parameters: []
4. call_fireman: Call this tool to interact with the fireman calling API. This API will call 119 to extinguish the fire. Parameters: []
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!"""messages = [{ 'role': 'system', 'content': system}, { 'role': 'user', 'content': '输入图片是/tmp/1.jpg，协助判断图片中是否存在着火点'}]resp = client.chat.completions.create( model=model_type, messages=messages, stop=['Observation:'], seed=42)response = resp.choices[0].message.contentprint(f'response: {response}')
# # 流式messages.append({'role': 'assistant', 'content': response + "\n[{'coordinate': [101.1, 200.9], 'on_fire': True}]"})print(messages)stream_resp = client.chat.completions.create( model=model_type, messages=messages, stop=['Observation:'], stream=True, seed=42)
print('response: ', end='')for chunk in stream_resp: print(chunk.choices[0].delta.content, end='', flush=True)print()## Output:# model_type: qwen-7b-chat# response: Thought: I need to check if there is fire in the image# Action: Use fire\_recognition API# Action Input: /tmp/1.jpg# Observation:# [{'role': 'system', 'content': 'Answer the following questions as best you can. You have access to the following APIs:\n1. 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"}]\n\n2. fire_alert: Call this tool to interact with the fire alert API. This API will start an alert to warn the building\'s administraters. Parameters: []\n\n3. call_police: Call this tool to interact with the police calling API. This API will call 110 to catch the thief. Parameters: []\n\n4. call_fireman: Call this tool to interact with the fireman calling API. This API will call 119 to extinguish the fire. Parameters: []\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of the above tools[fire_recognition, fire_alert, call_police, call_fireman]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can be repeated zero or more times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\nBegin!'}, {'role': 'user', 'content': '输入图片是/tmp/1.jpg，协助判断图片中是否存在着火点'}, {'role': 'assistant', 'content': "Thought: I need to check if there is fire in the image\nAction: Use fire\\_recognition API\nAction Input: /tmp/1.jpg\nObservation:\n[{'coordinate': [101.1, 200.9], 'on_fire': True}]"}]# response:# Final Answer: There is fire in the image at coordinates [101.1, 200.9]
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5.1 搭配 Modelscope-Agent 使用
结合Modelscope-Agent，微调模型用于搭建 Agent
本节针对 Modelscope-Agent 中的交互式框架 AgentFabric，微调小模型 qwen-7b-chat 使其具有 function call 能力
由于 ms-agent 中的 system prompt 与 Modelscope-Agent 中的 system prompt 格式不匹配，直接训练效果不佳，为此我们根据 ms-agent 转换格式得到新数据集ms_agent_for_agentfabric，现已集成到 SWIFT 中。其中ms-agent-for-agentfabric-default包含 3 万条由 ms-agent 转换的数据集，ms-agent-for-agentfabric-additional包含 488 条由开源的 AgentFabric 框架实际调用访问数据筛选得到
5.2 微调
将dataset换为ms-agent-for-agentfabric-default和ms-agent-for-agentfabric-addition
# Experimental environment: 8GPUnproc_per_node=8
PYTHONPATH=../../.. \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 \ --dataset ms-agent-for-agentfabric-default ms-agent-for-agentfabric-addition \ --train_dataset_mix_ratio 2.0 \ --train_dataset_sample -1 \ --num_train_epochs 2 \ --max_length 1500 \ --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 0.1 \ --learning_rate 5e-5 \ --gradient_accumulation_steps $(expr 32 / $nproc_per_node) \ --max_grad_norm 0.5 \ --warmup_ratio 0.03 \ --eval_steps 100 \ --save_steps 100 \ --save_total_limit 2 \ --logging_steps 10
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merge lora
CUDA_VISIBLE_DEVICES=0 swift export \ --ckpt_dir '/path/to/qwen-7b-chat/vx-xxx/checkpoint-xxx' --merge_lora true
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6. AgentFabric
git clone https://github.com/modelscope/modelscope-agent.gitcd modelscope-agent && pip install -r requirements.txt && pip install -r apps/agentfabric/requirements.txt
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6.1 部署模型
使用以下任意一种方式部署模型
CUDA_VISIBLE_DEVICES=0 swift deploy --ckpt_dir /path/to/qwen-7b-chat/vx-xxx/checkpoint-xxxx-merged
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python -m vllm.entrypoints.openai.api_server --model /path/to/qwen-7b-chat/vx-xxx/checkpoint-xxxx-merged --trust-remote-code
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"my-qwen-7b-chat": { "type": "openai", "model": "/path/to/qwen-7b-chat/vx-xxx/checkpoint-xxxx-merged", "api_base": "http://localhost:8000/v1", "is_chat": true, "is_function_call": false, "support_stream": false }
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注意，如果使用swift deploy部署，需要将"model"的值设为qwen-7b-chat
export PYTHONPATH=$PYTHONPATH:/path/to/your/modelscope-agentexport DASHSCOPE_API_KEY=your_api_keyexport AMAP_TOKEN=your_api_keycd modelscope-agent/apps/agentfabricpython app.py
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进入 AgentFabric 后，在配置(Configure)的模型中选择本地模型my-qwen-7b-chat
内置能力选择 agent 可以调用的 API, 这里选择Wanx Image Generation和高德天气
点击更新配置，等待配置完成后在右侧的输入栏中与 Agent 交互
天气查询
文生图
可以看到微调后的模型可以正确理解指令并调用工具
7. 总结
通过 SWIFT 支持的 Agent 训练能力，我们使用 ms-agent 和 ms-bench 对 qwen-7b-chat 模型进行了微调。可以看到微调后模型保留了通用知识问答能力，并在 system 字段增加了 API 的情况下可以正确调用并完成任务。需要注意的是：
训练从 LoRA 变为全参数训练，知识遗忘问题会更加严重，数据集混合比例需要实际测试调整
部分模型可能在训练后仍然调用效果不佳，可以测试该模型本身预训练能力是否扎实
Agent 训练集格式、语种有细节改变后，对应推理阶段的格式也需要相应调整，否则可能效果不佳
重要位置的\n等特殊字符比较重要，请注意推理和训练格式统一
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