基于 SWIFT 框架对 LLM 进行 Agent 能力微调的完整流程。涵盖环境安装、数据集格式准备（包含 Thought/Action/Observation 结构）、LoRA 微调参数配置及训练命令、推理测试方法以及通过 OpenAI 接口部署和集成 AgentFabric 的步骤。重点在于如何通过混合数据集避免知识遗忘，并实现 API 调用的稳定闭环。
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。
# 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
为训练 Agent 能力，提供了两个开源数据集：ms-agent 和 ms-bench。
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 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。"
}]
}
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 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 do
Action: 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 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!
API 格式：
Answer the following questions as best 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 do
Action: the action to take, should be one of the above tools[API 名称列表]
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!
Agent 数据集调用 API 的 response 的结构如下：
Action: hm_product_recommend
Action 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 answer
Final 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。
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 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。
| 数据集 | 条数 |
|---|---|
| ms-agent | 30000(全数据集) |
| ms-bench | 60000(抽样) |
| self-recognition | 3000(重复抽样) |
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：
| 超参数 | 值 |
|---|---|
| LR | 5e-5 |
| Epoch | 2 |
| lora_rank | 8 |
| lora_alpha | 32 |
| lora_target_modules | ALL |
| batch_size | 2 |
| gradient_accumulation_steps | 32 total |
运行命令和其他超参数如下：
# Experimental environment: 8GPU
nproc_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 my_agent \
--model_author developer \
--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
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。
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。
我们使用一个火焰报警场景作为测试用例：
Answer the following questions as best 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 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!
可以看到，人工输入 Observation 后模型答案并不正确。
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
运行命令后，改变 system 字段：
# 单行 system
<<< reset-system
<<< Answer the following questions as best 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!
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 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 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!#
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.
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。
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 qwn-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:
客户端：
from openai import OpenAI
client = OpenAI(
api_key='EMPTY',
base_url='http://localhost:8000/v1',
)
model_type = client.models.list().data[0].id
print(f'model_type: {model_type}')
system = """Answer the following questions as best 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 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!"""
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.content
print(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 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]
将 dataset 换为 ms-agent-for-agentfabric-default 和 ms-agent-for-agentfabric-addition
# Experimental environment: 8GPU
nproc_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 my_agent \
--model_author developer \
--gradient_checkpointing true \
--batch_size 2 \
--weight_decay 0.1 \
--learning_rate 5e-5 \
--gradient_accumulation_steps $(expr 32 / $nproc_per_node) \
--max_grad_norm 0.5 \
--warmup_ratio 0.03 \
--eval_steps 100 \
--save_steps 100 \
--save_total_limit 2 \
--logging_steps 10
merge lora
CUDA_VISIBLE_DEVICES=0 swift export \
--ckpt_dir '/path/to/qwen-7b-chat/vx-xxx/checkpoint-xxx' --merge_lora true
git clone https://github.com/modelscope/modelscope-agent.git
cd modelscope-agent && pip install -r requirements.txt && pip install -r apps/agentfabric/requirements.txt
使用以下任意一种方式部署模型
CUDA_VISIBLE_DEVICES=0 swift deploy --ckpt_dir /path/to/qwen-7b-chat/vx-xxx/checkpoint-xxxx-merged
python -m vllm.entrypoints.openai.api_server --model /path/to/qwen-7b-chat/vx-xxx/checkpoint-xxxx-merged --trust-remote-code
/path/to/modelscope-agent/apps/agentfabric/config/model_config.json 中，新增合并后的本地模型 "my-qwen-7b-chat": {
"type": "openai",
"model": "/path/to/qwen-7b-chat/vx-xxx/checkpoint-xxxx-merged",
"api_base": "http://localhost:8000/v1",
"is_chat": true,
"is_function_call": false,
"support_stream": false
}
注意，如果使用 swift deploy 部署，需要将 "model" 的值设为 qwen-7b-chat
export PYTHONPATH=$PYTHONPATH:/path/to/your/modelscope-agent
export DASHSCOPE_API_KEY=your_api_key
export AMAP_TOKEN=your_api_key
cd modelscope-agent/apps/agentfabric
python app.py
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
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：
\n 等特殊字符比较重要，请注意推理和训练格式统一
微信公众号「极客日志」，在微信中扫描左侧二维码关注。展示文案：极客日志 zeeklog
使用加密算法（如AES、TripleDES、Rabbit或RC4）加密和解密文本明文。 在线工具，加密/解密文本在线工具，online
生成新的随机RSA私钥和公钥pem证书。 在线工具，RSA密钥对生成器在线工具，online
基于 Mermaid.js 实时预览流程图、时序图等图表，支持源码编辑与即时渲染。 在线工具，Mermaid 预览与可视化编辑在线工具，online
解析常见 curl 参数并生成 fetch、axios、PHP curl 或 Python requests 示例代码。 在线工具，curl 转代码在线工具，online
将字符串编码和解码为其 Base64 格式表示形式即可。 在线工具，Base64 字符串编码/解码在线工具，online
将字符串、文件或图像转换为其 Base64 表示形式。 在线工具，Base64 文件转换器在线工具，online