7dabc4273d
- 在ChatConfig中添加get_current_talk_frequency()方法 - 在ExpressionConfig中添加get_expression_config_for_chat()方法，但是模型配置文件依旧被架空力😭😭😭我解决不了，帮帮我！雅诺狐姐姐！😭😭😭
218 lines
7.2 KiB
TOML
218 lines
7.2 KiB
TOML
[inner]
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version = "1.2.6"
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# 配置文件版本号迭代规则同bot_config.toml
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[[api_providers]] # API服务提供商（可以配置多个）
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name = "DeepSeek" # API服务商名称（可随意命名，在models的api-provider中需使用这个命名）
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base_url = "https://api.deepseek.cn/v1" # API服务商的BaseURL
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api_key = "your-api-key-here" # API密钥（请替换为实际的API密钥）
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client_type = "openai" # 请求客户端（可选，默认值为"openai"，使用gimini等Google系模型时请配置为"gemini"）
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max_retry = 2 # 最大重试次数（单个模型API调用失败，最多重试的次数）
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timeout = 30 # API请求超时时间（单位：秒）
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retry_interval = 10 # 重试间隔时间（单位：秒）
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[[api_providers]] # SiliconFlow的API服务商配置
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name = "SiliconFlow"
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base_url = "https://api.siliconflow.cn/v1"
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api_key = "your-siliconflow-api-key-here"
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client_type = "openai"
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max_retry = 2
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timeout = 30
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retry_interval = 10
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[[api_providers]] # 特殊：Google的Gimini使用特殊API，与OpenAI格式不兼容，需要配置client为"gemini"
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name = "Google"
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base_url = "https://api.google.com/v1"
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api_key = "your-google-api-key-1"
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client_type = "gemini"
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max_retry = 2
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timeout = 30
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retry_interval = 10
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# 内容混淆功能示例配置（可选）
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[[api_providers]]
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name = "ExampleProviderWithObfuscation" # 启用混淆功能的API提供商示例
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base_url = "https://api.example.com/v1"
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api_key = "your-api-key-here"
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client_type = "openai"
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max_retry = 2
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timeout = 30
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retry_interval = 10
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enable_content_obfuscation = true # 启用内容混淆功能
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obfuscation_intensity = 2 # 混淆强度（1-3级，1=低强度，2=中强度，3=高强度）
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[[models]] # 模型（可以配置多个）
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model_identifier = "deepseek-chat" # 模型标识符（API服务商提供的模型标识符）
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name = "deepseek-v3" # 模型名称（可随意命名，在后面中需使用这个命名）
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api_provider = "DeepSeek" # API服务商名称（对应在api_providers中配置的服务商名称）
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price_in = 2.0 # 输入价格（用于API调用统计，单位：元/ M token）（可选，若无该字段，默认值为0）
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price_out = 8.0 # 输出价格（用于API调用统计，单位：元/ M token）（可选，若无该字段，默认值为0）
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#force_stream_mode = true # 强制流式输出模式（若模型不支持非流式输出，请取消该注释，启用强制流式输出，若无该字段，默认值为false）
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[[models]]
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model_identifier = "Pro/deepseek-ai/DeepSeek-V3"
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name = "siliconflow-deepseek-v3"
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api_provider = "SiliconFlow"
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price_in = 2.0
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price_out = 8.0
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[[models]]
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model_identifier = "Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
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name = "deepseek-r1-distill-qwen-32b"
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api_provider = "SiliconFlow"
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price_in = 4.0
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price_out = 16.0
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[[models]]
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model_identifier = "Qwen/Qwen3-8B"
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name = "qwen3-8b"
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api_provider = "SiliconFlow"
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price_in = 0
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price_out = 0
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[models.extra_params] # 可选的额外参数配置
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enable_thinking = false # 不启用思考
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[[models]]
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model_identifier = "Qwen/Qwen3-14B"
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name = "qwen3-14b"
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api_provider = "SiliconFlow"
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price_in = 0.5
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price_out = 2.0
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[models.extra_params] # 可选的额外参数配置
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enable_thinking = false # 不启用思考
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[[models]]
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model_identifier = "Qwen/Qwen3-30B-A3B"
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name = "qwen3-30b"
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api_provider = "SiliconFlow"
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price_in = 0.7
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price_out = 2.8
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[models.extra_params] # 可选的额外参数配置
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enable_thinking = false # 不启用思考
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[[models]]
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model_identifier = "Qwen/Qwen2.5-VL-72B-Instruct"
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name = "qwen2.5-vl-72b"
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api_provider = "SiliconFlow"
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price_in = 4.13
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price_out = 4.13
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[[models]]
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model_identifier = "FunAudioLLM/SenseVoiceSmall"
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name = "sensevoice-small"
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api_provider = "SiliconFlow"
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price_in = 0
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price_out = 0
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[[models]]
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model_identifier = "BAAI/bge-m3"
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name = "bge-m3"
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api_provider = "SiliconFlow"
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price_in = 0
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price_out = 0
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[[models]]
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model_identifier = "moonshotai/Kimi-K2-Instruct"
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name = "moonshotai-Kimi-K2-Instruct"
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api_provider = "SiliconFlow"
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price_in = 4.0
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price_out = 16.0
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[model_task_config.utils] # 在麦麦的一些组件中使用的模型，例如表情包模块，取名模块，关系模块，是麦麦必须的模型
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model_list = ["siliconflow-deepseek-v3"] # 使用的模型列表，每个子项对应上面的模型名称(name)
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temperature = 0.2 # 模型温度，新V3建议0.1-0.3
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max_tokens = 800 # 最大输出token数
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#concurrency_count = 2 # 并发请求数量，默认为1（不并发），设置为2或更高启用并发
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[model_task_config.utils_small] # 在麦麦的一些组件中使用的小模型，消耗量较大，建议使用速度较快的小模型
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model_list = ["qwen3-8b"]
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temperature = 0.7
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max_tokens = 800
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[model_task_config.replyer_1] # 首要回复模型，还用于表达器和表达方式学习
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model_list = ["siliconflow-deepseek-v3"]
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temperature = 0.2 # 模型温度，新V3建议0.1-0.3
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max_tokens = 800
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[model_task_config.replyer_2] # 次要回复模型
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model_list = ["siliconflow-deepseek-v3"]
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temperature = 0.7
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max_tokens = 800
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[model_task_config.planner] #决策：负责决定麦麦该做什么的模型
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model_list = ["siliconflow-deepseek-v3"]
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temperature = 0.3
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max_tokens = 800
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[model_task_config.emotion] #负责麦麦的情绪变化
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model_list = ["siliconflow-deepseek-v3"]
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temperature = 0.3
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max_tokens = 800
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[model_task_config.mood] #负责麦麦的心情变化
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model_list = ["siliconflow-deepseek-v3"]
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temperature = 0.3
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max_tokens = 800
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[model_task_config.maizone] # maizone模型
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model_list = ["siliconflow-deepseek-v3"]
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temperature = 0.7
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max_tokens = 800
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[model_task_config.vlm] # 图像识别模型
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model_list = ["qwen2.5-vl-72b"]
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max_tokens = 800
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[model_task_config.emoji_vlm] # 专用表情包识别模型
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model_list = ["qwen2.5-vl-72b"]
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max_tokens = 800
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[model_task_config.utils_video] # 专用视频分析模型
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model_list = ["qwen2.5-vl-72b"]
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temperature = 0.3
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max_tokens = 1500
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[model_task_config.voice] # 语音识别模型
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model_list = ["sensevoice-small"]
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[model_task_config.tool_use] #工具调用模型，需要使用支持工具调用的模型
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model_list = ["qwen3-14b"]
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temperature = 0.7
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max_tokens = 800
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[model_task_config.schedule_generator]#日程表生成模型
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model_list = ["deepseek-v3"]
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temperature = 0.7
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max_tokens = 1000
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[model_task_config.anti_injection] # 反注入检测专用模型
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model_list = ["moonshotai-Kimi-K2-Instruct"] # 使用快速的小模型进行检测
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temperature = 0.1 # 低温度确保检测结果稳定
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max_tokens = 200 # 检测结果不需要太长的输出
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#嵌入模型
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[model_task_config.embedding]
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model_list = ["bge-m3"]
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#------------LPMM知识库模型------------
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[model_task_config.lpmm_entity_extract] # 实体提取模型
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model_list = ["siliconflow-deepseek-v3"]
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temperature = 0.2
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max_tokens = 800
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[model_task_config.lpmm_rdf_build] # RDF构建模型
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model_list = ["siliconflow-deepseek-v3"]
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temperature = 0.2
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max_tokens = 800
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[model_task_config.lpmm_qa] # 问答模型
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model_list = ["deepseek-r1-distill-qwen-32b"]
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temperature = 0.7
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max_tokens = 800
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