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Langchain-Chatchat/configs/model_config.py.example

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log_path: "logs"
log_level: "DEBUG"
api_server:
host: "127.0.0.1"
port: 8000
publish_server:
host: "127.0.0.1"
port: 8001
openai_plugins_folder:
- "openai_plugins"
openai_plugins_load_folder:
- "configs"
plugins:
- openai:
name: "openai"
- fastchat:
name: "fastchat"
logdir: "logs"
# LLM 运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。
llm_device: "auto"
model_names:
- "internlm2-chat-7b"
run_controller:
host: "127.0.0.1"
port: 20001
dispatch_method: "shortest_queue"
run_openai_api:
host: "127.0.0.1"
port: 20000
fschat_model_workers:
- default:
host: "127.0.0.1"
port: 20002
device: "auto"
infer_turbo: False
# model_worker多卡加载需要配置的参数
# "gpus": None, # 使用的GPU，以str的格式指定，如"0,1"，如失效请使用CUDA_VISIBLE_DEVICES="0,1"等形式指定
# "num_gpus": 1, # 使用GPU的数量
# "max_gpu_memory": "20GiB", # 每个GPU占用的最大显存
# 以下为model_worker非常用参数，可根据需要配置
# "load_8bit": False, # 开启8bit量化
# "cpu_offloading": None,
# "gptq_ckpt": None,
# "gptq_wbits": 16,
# "gptq_groupsize": -1,
# "gptq_act_order": False,
# "awq_ckpt": None,
# "awq_wbits": 16,
# "awq_groupsize": -1,
# "model_names": LLM_MODELS,
# "conv_template": None,
# "limit_worker_concurrency": 5,
# "stream_interval": 2,
# "no_register": False,
# "embed_in_truncate": False,
# 以下为vllm_worker配置参数,注意使用vllm必须有gpu，仅在Linux测试通过
# tokenizer = model_path # 如果tokenizer与model_path不一致在此处添加
# 'tokenizer_mode':'auto',
# 'trust_remote_code':True,
# 'download_dir':None,
# 'load_format':'auto',
# 'dtype':'auto',
# 'seed':0,
# 'worker_use_ray':False,
# 'pipeline_parallel_size':1,
# 'tensor_parallel_size':1,
# 'block_size':16,
# 'swap_space':4 , # GiB
# 'gpu_memory_utilization':0.90,
# 'max_num_batched_tokens':2560,
# 'max_num_seqs':256,
# 'disable_log_stats':False,
# 'conv_template':None,
# 'limit_worker_concurrency':5,
# 'no_register':False,
# 'num_gpus': 1
# 'engine_use_ray': False,
# 'disable_log_requests': False
- chatglm3-6b:
host: "127.0.0.1"
device: "cuda"
port: 20009
- internlm2-chat-7b:
host: "127.0.0.1"
device: "cuda"
port: 20009
# 以下配置可以不用修改，在model_config中设置启动的模型
- zhipu-api:
port: 21001
- minimax-api:
port: 21002
- xinghuo-api:
port: 21003
- qianfan-api:
port: 21004
- fangzhou-api:
port: 21005
- qwen-api:
port: 21006
- baichuan-api:
port: 21007
- azure-api:
port: 21008
- tiangong-api:
port: 21009
online_llm_model:
# 线上模型。请在server_config中为每个在线API设置不同的端口
- "openai-api":
"model_name": "gpt-3.5-turbo"
"api_base_url": "https://api.openai.com/v1"
"api_key": ""
"openai_proxy": ""
# 具体注册及api key获取请前往 http://open.bigmodel.cn
- "zhipu-api":
"api_key": ""
"version": "chatglm_turbo" # 可选包括 "chatglm_turbo"
"provider": "ChatGLMWorker"
# 具体注册及api key获取请前往 https://api.minimax.chat/
- "minimax-api":
"group_id": ""
"api_key": ""
"is_pro": False
"provider": "MiniMaxWorker"
# 具体注册及api key获取请前往 https://xinghuo.xfyun.cn/
- "xinghuo-api":
"APPID": ""
"APISecret": ""
"api_key": ""
"version": "v1.5" # 你使用的讯飞星火大模型版本，可选包括 "v3.0", "v1.5", "v2.0"
"provider": "XingHuoWorker"
# 百度千帆 API，申请方式请参考 https://cloud.baidu.com/doc/WENXINWORKSHOP/s/4lilb2lpf
- "qianfan-api":
"version": "ERNIE-Bot" # 注意大小写。当前支持 "ERNIE-Bot" 或 "ERNIE-Bot-turbo"， 更多的见官方文档。
"version_url": "" # 也可以不填写version，直接填写在千帆申请模型发布的API地址
"api_key": ""
"secret_key": ""
"provider": "QianFanWorker"
# 火山方舟 API，文档参考 https://www.volcengine.com/docs/82379
- "fangzhou-api":
"version": "chatglm-6b-model" # 当前支持 "chatglm-6b-model"， 更多的见文档模型支持列表中方舟部分。
"version_url": "" # 可以不填写version，直接填写在方舟申请模型发布的API地址
"api_key": ""
"secret_key": ""
"provider": "FangZhouWorker"
# 阿里云通义千问 API，文档参考 https://help.aliyun.com/zh/dashscope/developer-reference/api-details
- "qwen-api":
"version": "qwen-turbo" # 可选包括 "qwen-turbo", "qwen-plus"
"api_key": "" # 请在阿里云控制台模型服务灵积API-KEY管理页面创建
"provider": "QwenWorker"
# 百川 API，申请方式请参考 https://www.baichuan-ai.com/home#api-enter
- "baichuan-api":
"version": "Baichuan2-53B" # 当前支持 "Baichuan2-53B"， 见官方文档。
"api_key": ""
"secret_key": ""
"provider": "BaiChuanWorker"
# Azure API
- "azure-api":
"deployment_name": "" # 部署容器的名字
"resource_name": "" # https://{resource_name}.openai.azure.com/openai/ 填写resource_name的部分，其他部分不要填写
"api_version": "" # API的版本，不是模型版本
"api_key": ""
"provider": "AzureWorker"
# 昆仑万维天工 API https://model-platform.tiangong.cn/
- "tiangong-api":
"version": "SkyChat-MegaVerse"
"api_key": ""
"secret_key": ""
"provider": "TianGongWorker"
"llm_model":
"chatglm2-6b": "THUDM/chatglm2-6b"
"chatglm2-6b-32k": "THUDM/chatglm2-6b-32k"
"chatglm3-6b": "THUDM/chatglm3-6b"
"chatglm3-6b-32k": "THUDM/chatglm3-6b-32k"
"Llama-2-7b-chat-hf": "meta-llama/Llama-2-7b-chat-hf"
"Llama-2-13b-chat-hf": "meta-llama/Llama-2-13b-chat-hf"
"Llama-2-70b-chat-hf": "meta-llama/Llama-2-70b-chat-hf"
"Qwen-1_8B-Chat": "/media/checkpoint/Qwen-1_8B-Chat"
"Qwen-7B-Chat": "Qwen/Qwen-7B-Chat"
"Qwen-14B-Chat": "Qwen/Qwen-14B-Chat"
"Qwen-72B-Chat": "Qwen/Qwen-72B-Chat"
"baichuan-7b-chat": "baichuan-inc/Baichuan-7B-Chat"
"baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat"
"baichuan2-7b-chat": "baichuan-inc/Baichuan2-7B-Chat"
"baichuan2-13b-chat": "baichuan-inc/Baichuan2-13B-Chat"
"internlm-7b": "internlm/internlm-7b"
"internlm-chat-7b": "internlm/internlm-chat-7b"
"internlm2-chat-7b": "internlm/internlm2-chat-7b"
"internlm2-chat-20b": "internlm/internlm2-chat-20b"
"BlueLM-7B-Chat": "vivo-ai/BlueLM-7B-Chat"
"BlueLM-7B-Chat-32k": "vivo-ai/BlueLM-7B-Chat-32k"
"Yi-34B-Chat": "https://huggingface.co/01-ai/Yi-34B-Chat"
"agentlm-7b": "THUDM/agentlm-7b"
"agentlm-13b": "THUDM/agentlm-13b"
"agentlm-70b": "THUDM/agentlm-70b"
"falcon-7b": "tiiuae/falcon-7b"
"falcon-40b": "tiiuae/falcon-40b"
"falcon-rw-7b": "tiiuae/falcon-rw-7b"
"aquila-7b": "BAAI/Aquila-7B"
"aquilachat-7b": "BAAI/AquilaChat-7B"
"open_llama_13b": "openlm-research/open_llama_13b"
"vicuna-13b-v1.5": "lmsys/vicuna-13b-v1.5"
"koala": "young-geng/koala"
"mpt-7b": "mosaicml/mpt-7b"
"mpt-7b-storywriter": "mosaicml/mpt-7b-storywriter"
"mpt-30b": "mosaicml/mpt-30b"
"opt-66b": "facebook/opt-66b"
"opt-iml-max-30b": "facebook/opt-iml-max-30b"
"gpt2": "gpt2"
"gpt2-xl": "gpt2-xl"
"gpt-j-6b": "EleutherAI/gpt-j-6b"
"gpt4all-j": "nomic-ai/gpt4all-j"
"gpt-neox-20b": "EleutherAI/gpt-neox-20b"
"pythia-12b": "EleutherAI/pythia-12b"
"oasst-sft-4-pythia-12b-epoch-3.5": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"
"dolly-v2-12b": "databricks/dolly-v2-12b"
"stablelm-tuned-alpha-7b": "stabilityai/stablelm-tuned-alpha-7b"
vllm_model_dict:
"chatglm2-6b": "THUDM/chatglm2-6b"
"chatglm2-6b-32k": "THUDM/chatglm2-6b-32k"
"chatglm3-6b": "THUDM/chatglm3-6b"
"chatglm3-6b-32k": "THUDM/chatglm3-6b-32k"
"Llama-2-7b-chat-hf": "meta-llama/Llama-2-7b-chat-hf"
"Llama-2-13b-chat-hf": "meta-llama/Llama-2-13b-chat-hf"
"Llama-2-70b-chat-hf": "meta-llama/Llama-2-70b-chat-hf"
"Qwen-1_8B-Chat": "Qwen/Qwen-1_8B-Chat"
"Qwen-7B-Chat": "Qwen/Qwen-7B-Chat"
"Qwen-14B-Chat": "Qwen/Qwen-14B-Chat"
"Qwen-72B-Chat": "Qwen/Qwen-72B-Chat"
"baichuan-7b-chat": "baichuan-inc/Baichuan-7B-Chat"
"baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat"
"baichuan2-7b-chat": "baichuan-inc/Baichuan-7B-Chat"
"baichuan2-13b-chat": "baichuan-inc/Baichuan-13B-Chat"
"BlueLM-7B-Chat": "vivo-ai/BlueLM-7B-Chat"
"BlueLM-7B-Chat-32k": "vivo-ai/BlueLM-7B-Chat-32k"
"internlm-7b": "internlm/internlm-7b"
"internlm-chat-7b": "internlm/internlm-chat-7b"
"internlm2-chat-7b": "internlm/Models/internlm2-chat-7b"
"internlm2-chat-20b": "internlm/Models/internlm2-chat-20b"
"aquila-7b": "BAAI/Aquila-7B"
"aquilachat-7b": "BAAI/AquilaChat-7B"
"falcon-7b": "tiiuae/falcon-7b"
"falcon-40b": "tiiuae/falcon-40b"
"falcon-rw-7b": "tiiuae/falcon-rw-7b"
"gpt2": "gpt2"
"gpt2-xl": "gpt2-xl"
"gpt-j-6b": "EleutherAI/gpt-j-6b"
"gpt4all-j": "nomic-ai/gpt4all-j"
"gpt-neox-20b": "EleutherAI/gpt-neox-20b"
"pythia-12b": "EleutherAI/pythia-12b"
"oasst-sft-4-pythia-12b-epoch-3.5": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"
"dolly-v2-12b": "databricks/dolly-v2-12b"
"stablelm-tuned-alpha-7b": "stabilityai/stablelm-tuned-alpha-7b"
"open_llama_13b": "openlm-research/open_llama_13b"
"vicuna-13b-v1.3": "lmsys/vicuna-13b-v1.3"
"koala": "young-geng/koala"
"mpt-7b": "mosaicml/mpt-7b"
"mpt-7b-storywriter": "mosaicml/mpt-7b-storywriter"
"mpt-30b": "mosaicml/mpt-30b"
"opt-66b": "facebook/opt-66b"
"opt-iml-max-30b": "facebook/opt-iml-max-30b"
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