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test_ai/configs/model_config.py.example

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2024-01-12 10:26:57 +08:00
import os
# 可以指定一个绝对路径，统一存放所有的Embedding和LLM模型。
# 每个模型可以是一个单独的目录，也可以是某个目录下的二级子目录。
# 如果模型目录名称和 MODEL_PATH 中的 key 或 value 相同，程序会自动检测加载，无需修改 MODEL_PATH 中的路径。
MODEL_ROOT_PATH = ""
# 选用的 Embedding 名称
EMBEDDING_MODEL = "m3e-base" # bge-large-zh
# Embedding 模型运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。
EMBEDDING_DEVICE = "auto"
# 如果需要在 EMBEDDING_MODEL 中增加自定义的关键字时配置
EMBEDDING_KEYWORD_FILE = "keywords.txt"
EMBEDDING_MODEL_OUTPUT_PATH = "output"
# 要运行的 LLM 名称，可以包括本地模型和在线模型。
# 第一个将作为 API 和 WEBUI 的默认模型
LLM_MODELS = ["chatglm2-6b", "zhipu-api", "openai-api"]
# AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0])
Agent_MODEL = None
# 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"
# 历史对话轮数
HISTORY_LEN = 3
# 大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度
MAX_TOKENS = None
# LLM通用对话参数
TEMPERATURE = 0.7
# TOP_P = 0.95 # ChatOpenAI暂不支持该参数
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-35-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",
},
}
# 在以下字典中修改属性值，以指定本地embedding模型存储位置。支持3种设置方法：
# 1、将对应的值修改为模型绝对路径
# 2、不修改此处的值（以 text2vec 为例）：
# 2.1 如果{MODEL_ROOT_PATH}下存在如下任一子目录：
# - text2vec
# - GanymedeNil/text2vec-large-chinese
# - text2vec-large-chinese
# 2.2 如果以上本地路径不存在，则使用huggingface模型
MODEL_PATH = {
"embed_model": {
"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
"ernie-base": "nghuyong/ernie-3.0-base-zh",
"text2vec-base": "shibing624/text2vec-base-chinese",
"text2vec": "GanymedeNil/text2vec-large-chinese",
"text2vec-paraphrase": "shibing624/text2vec-base-chinese-paraphrase",
"text2vec-sentence": "shibing624/text2vec-base-chinese-sentence",
"text2vec-multilingual": "shibing624/text2vec-base-multilingual",
"text2vec-bge-large-chinese": "shibing624/text2vec-bge-large-chinese",
"m3e-small": "moka-ai/m3e-small",
"m3e-base": "moka-ai/m3e-base",
"m3e-large": "moka-ai/m3e-large",
"bge-small-zh": "BAAI/bge-small-zh",
"bge-base-zh": "BAAI/bge-base-zh",
"bge-large-zh": "BAAI/bge-large-zh",
"bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct",
"bge-base-zh-v1.5": "BAAI/bge-base-zh-v1.5",
"bge-large-zh-v1.5": "BAAI/bge-large-zh-v1.5",
"piccolo-base-zh": "sensenova/piccolo-base-zh",
"piccolo-large-zh": "sensenova/piccolo-large-zh",
"text-embedding-ada-002": "your OPENAI_API_KEY",
},
"llm_model": {
# 以下部分模型并未完全测试，仅根据fastchat和vllm模型的模型列表推定支持
"chatglm2-6b": "THUDM/chatglm2-6b",
"chatglm2-6b-32k": "THUDM/chatglm2-6b-32k",
"baichuan2-13b": "baichuan-inc/Baichuan2-13B-Chat",
"baichuan2-7b": "baichuan-inc/Baichuan2-7B-Chat",
"baichuan-7b": "baichuan-inc/Baichuan-7B",
"baichuan-13b": "baichuan-inc/Baichuan-13B",
'baichuan-13b-chat': 'baichuan-inc/Baichuan-13B-Chat',
"aquila-7b": "BAAI/Aquila-7B",
"aquilachat-7b": "BAAI/AquilaChat-7B",
"internlm-7b": "internlm/internlm-7b",
"internlm-chat-7b": "internlm/internlm-chat-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",
"Llama-2-13b-hf": "meta-llama/Llama-2-13b-hf",
"Llama-2-70b-hf": "meta-llama/Llama-2-70b-hf",
"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",
"Qwen-7B": "Qwen/Qwen-7B",
"Qwen-14B": "Qwen/Qwen-14B",
"Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
"Qwen-14B-Chat": "Qwen/Qwen-14B-Chat",
"Qwen-14B-Chat-Int8": "Qwen/Qwen-14B-Chat-Int8", # 确保已经安装了auto-gptq optimum flash-attn
"Qwen-14B-Chat-Int4": "Qwen/Qwen-14B-Chat-Int4", # 确保已经安装了auto-gptq optimum flash-attn
},
}
# 通常情况下不需要更改以下内容
# nltk 模型存储路径
NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
VLLM_MODEL_DICT = {
"aquila-7b": "BAAI/Aquila-7B",
"aquilachat-7b": "BAAI/AquilaChat-7B",
"baichuan-7b": "baichuan-inc/Baichuan-7B",
"baichuan-13b": "baichuan-inc/Baichuan-13B",
'baichuan-13b-chat': 'baichuan-inc/Baichuan-13B-Chat',
# 注意：bloom系列的tokenizer与model是分离的，因此虽然vllm支持，但与fschat框架不兼容
# "bloom":"bigscience/bloom",
# "bloomz":"bigscience/bloomz",
# "bloomz-560m":"bigscience/bloomz-560m",
# "bloomz-7b1":"bigscience/bloomz-7b1",
# "bloomz-1b7":"bigscience/bloomz-1b7",
"internlm-7b": "internlm/internlm-7b",
"internlm-chat-7b": "internlm/internlm-chat-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",
"Llama-2-13b-hf": "meta-llama/Llama-2-13b-hf",
"Llama-2-70b-hf": "meta-llama/Llama-2-70b-hf",
"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",
"Qwen-7B": "Qwen/Qwen-7B",
"Qwen-14B": "Qwen/Qwen-14B",
"Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
"Qwen-14B-Chat": "Qwen/Qwen-14B-Chat",
"agentlm-7b": "THUDM/agentlm-7b",
"agentlm-13b": "THUDM/agentlm-13b",
"agentlm-70b": "THUDM/agentlm-70b",
}
# 你认为支持Agent能力的模型，可以在这里添加，添加后不会出现可视化界面的警告
SUPPORT_AGENT_MODEL = [
"azure-api",
"openai-api",
"claude-api",
"zhipu-api",
"qwen-api",
"Qwen",
"baichuan-api",
"agentlm",
"chatglm3",
"xinghuo-api",
]