274 lines
10 KiB
Python
274 lines
10 KiB
Python
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import os
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# 可以指定一个绝对路径，统一存放所有的Embedding和LLM模型。
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# 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。
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# 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。
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MODEL_ROOT_PATH = ""
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# 选用的 Embedding 名称
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EMBEDDING_MODEL = "m3e-base" # bge-large-zh
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# 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。
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EMBEDDING_DEVICE = "auto"
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# 如果需要在 EMBEDDING_MODEL 中增加自定义的关键字时配置
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EMBEDDING_KEYWORD_FILE = "keywords.txt"
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EMBEDDING_MODEL_OUTPUT_PATH = "output"
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# 要运行的 LLM 名称，可以包括本地模型和在线模型。
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# 第一个将作为 API 和 WEBUI 的默认模型
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LLM_MODELS = ["chatglm3-6b"]
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# 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])
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Agent_MODEL = None
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# 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。
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LLM_DEVICE = "auto"
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# 历史对话轮数
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HISTORY_LEN = 3
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# 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
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MAX_TOKENS = None
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# LLM通用对话参数
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TEMPERATURE = 0.7
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# TOP_P = 0.95 # ChatOpenAI暂不支持该参数
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ONLINE_LLM_MODEL = {
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# 线上模型。请在server_config中为每个在线API设置不同的端口
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"openai-api": {
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"model_name": "gpt-35-turbo",
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"api_base_url": "https://api.openai.com/v1",
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"api_key": "",
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"openai_proxy": "",
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},
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# 具体注册及api key获取请前往 http://open.bigmodel.cn
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"zhipu-api": {
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"api_key": "",
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"version": "chatglm_turbo", # 可选包括 "chatglm_turbo"
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"provider": "ChatGLMWorker",
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},
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# 具体注册及api key获取请前往 https://api.minimax.chat/
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"minimax-api": {
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"group_id": "",
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"api_key": "",
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"is_pro": False,
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"provider": "MiniMaxWorker",
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},
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# 具体注册及api key获取请前往 https://xinghuo.xfyun.cn/
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"xinghuo-api": {
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"APPID": "",
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"APISecret": "",
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"api_key": "",
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"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"
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"provider": "XingHuoWorker",
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},
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# 百度千帆 API，申请方式请参考 https://cloud.baidu.com/doc/WENXINWORKSHOP/s/4lilb2lpf
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"qianfan-api": {
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"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。
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"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
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"api_key": "",
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"secret_key": "",
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"provider": "QianFanWorker",
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},
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# 火山方舟 API，文档参考 https://www.volcengine.com/docs/82379
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"fangzhou-api": {
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"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。
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"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
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"api_key": "",
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"secret_key": "",
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"provider": "FangZhouWorker",
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},
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# 阿里云通义千问 API，文档参考 https://help.aliyun.com/zh/dashscope/developer-reference/api-details
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"qwen-api": {
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"version": "qwen-turbo", # 可选包括 "qwen-turbo", "qwen-plus"
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"api_key": "", # 请在阿里云控制台模型服务灵积API-KEY管理页面创建
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"provider": "QwenWorker",
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},
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# 百川 API，申请方式请参考 https://www.baichuan-ai.com/home#api-enter
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"baichuan-api": {
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"version": "Baichuan2-53B", # 当前支持 "Baichuan2-53B"， 见官方文档。
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"api_key": "",
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"secret_key": "",
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"provider": "BaiChuanWorker",
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},
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# Azure API
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"azure-api": {
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"deployment_name": "", # 部署容器的名字
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"resource_name": "", # https://{resource_name}.openai.azure.com/openai/ 填写resource_name的部分，其他部分不要填写
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"api_version": "", # API的版本，不是模型版本
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"api_key": "",
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"provider": "AzureWorker",
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},
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}
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# 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：
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# 1、将对应的值修改为模型绝对路径
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# 2、不修改此处的值（以 text2vec 为例）：
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# 2.1 如果{MODEL_ROOT_PATH}下存在如下任一子目录：
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# - text2vec
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# - GanymedeNil/text2vec-large-chinese
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# - text2vec-large-chinese
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# 2.2 如果以上本地路径不存在，则使用huggingface模型
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MODEL_PATH = {
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"embed_model": {
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"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
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"ernie-base": "nghuyong/ernie-3.0-base-zh",
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"text2vec-base": "shibing624/text2vec-base-chinese",
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"text2vec": "GanymedeNil/text2vec-large-chinese",
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"text2vec-paraphrase": "shibing624/text2vec-base-chinese-paraphrase",
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"text2vec-sentence": "shibing624/text2vec-base-chinese-sentence",
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"text2vec-multilingual": "shibing624/text2vec-base-multilingual",
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"text2vec-bge-large-chinese": "shibing624/text2vec-bge-large-chinese",
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"m3e-small": "moka-ai/m3e-small",
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"m3e-base": "moka-ai/m3e-base",
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"m3e-large": "moka-ai/m3e-large",
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"bge-small-zh": "BAAI/bge-small-zh",
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"bge-base-zh": "BAAI/bge-base-zh",
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"bge-large-zh": "BAAI/bge-large-zh",
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"bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct",
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"bge-base-zh-v1.5": "BAAI/bge-base-zh-v1.5",
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"bge-large-zh-v1.5": "BAAI/bge-large-zh-v1.5",
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"piccolo-base-zh": "sensenova/piccolo-base-zh",
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"piccolo-large-zh": "sensenova/piccolo-large-zh",
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"text-embedding-ada-002": "your OPENAI_API_KEY",
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},
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"llm_model": {
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# 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
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"chatglm2-6b": "THUDM/chatglm2-6b",
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"chatglm2-6b-32k": "THUDM/chatglm2-6b-32k",
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"baichuan2-13b": "baichuan-inc/Baichuan2-13B-Chat",
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"baichuan2-7b": "baichuan-inc/Baichuan2-7B-Chat",
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"baichuan-7b": "baichuan-inc/Baichuan-7B",
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"baichuan-13b": "baichuan-inc/Baichuan-13B",
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'baichuan-13b-chat': 'baichuan-inc/Baichuan-13B-Chat',
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"aquila-7b": "BAAI/Aquila-7B",
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"aquilachat-7b": "BAAI/AquilaChat-7B",
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"internlm-7b": "internlm/internlm-7b",
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"internlm-chat-7b": "internlm/internlm-chat-7b",
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"falcon-7b": "tiiuae/falcon-7b",
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"falcon-40b": "tiiuae/falcon-40b",
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"falcon-rw-7b": "tiiuae/falcon-rw-7b",
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"gpt2": "gpt2",
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"gpt2-xl": "gpt2-xl",
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"gpt-j-6b": "EleutherAI/gpt-j-6b",
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"gpt4all-j": "nomic-ai/gpt4all-j",
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"gpt-neox-20b": "EleutherAI/gpt-neox-20b",
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"pythia-12b": "EleutherAI/pythia-12b",
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"oasst-sft-4-pythia-12b-epoch-3.5": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
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"dolly-v2-12b": "databricks/dolly-v2-12b",
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"stablelm-tuned-alpha-7b": "stabilityai/stablelm-tuned-alpha-7b",
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"Llama-2-13b-hf": "meta-llama/Llama-2-13b-hf",
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"Llama-2-70b-hf": "meta-llama/Llama-2-70b-hf",
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"open_llama_13b": "openlm-research/open_llama_13b",
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"vicuna-13b-v1.3": "lmsys/vicuna-13b-v1.3",
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"koala": "young-geng/koala",
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"mpt-7b": "mosaicml/mpt-7b",
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"mpt-7b-storywriter": "mosaicml/mpt-7b-storywriter",
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"mpt-30b": "mosaicml/mpt-30b",
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"opt-66b": "facebook/opt-66b",
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"opt-iml-max-30b": "facebook/opt-iml-max-30b",
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"Qwen-7B": "Qwen/Qwen-7B",
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"Qwen-14B": "Qwen/Qwen-14B",
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"Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
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"Qwen-14B-Chat": "Qwen/Qwen-14B-Chat",
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"Qwen-14B-Chat-Int8": "Qwen/Qwen-14B-Chat-Int8", # 确保已经安装了auto-gptq optimum flash-attn
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"Qwen-14B-Chat-Int4": "Qwen/Qwen-14B-Chat-Int4", # 确保已经安装了auto-gptq optimum flash-attn
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},
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}
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# 通常情况下不需要更改以下内容
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# nltk 模型存储路径
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NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
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VLLM_MODEL_DICT = {
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"aquila-7b": "BAAI/Aquila-7B",
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"aquilachat-7b": "BAAI/AquilaChat-7B",
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"baichuan-7b": "baichuan-inc/Baichuan-7B",
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"baichuan-13b": "baichuan-inc/Baichuan-13B",
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'baichuan-13b-chat': 'baichuan-inc/Baichuan-13B-Chat',
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# 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
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# "bloom":"bigscience/bloom",
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# "bloomz":"bigscience/bloomz",
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# "bloomz-560m":"bigscience/bloomz-560m",
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# "bloomz-7b1":"bigscience/bloomz-7b1",
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# "bloomz-1b7":"bigscience/bloomz-1b7",
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"internlm-7b": "internlm/internlm-7b",
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"internlm-chat-7b": "internlm/internlm-chat-7b",
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"falcon-7b": "tiiuae/falcon-7b",
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"falcon-40b": "tiiuae/falcon-40b",
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"falcon-rw-7b": "tiiuae/falcon-rw-7b",
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"gpt2": "gpt2",
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"gpt2-xl": "gpt2-xl",
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"gpt-j-6b": "EleutherAI/gpt-j-6b",
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"gpt4all-j": "nomic-ai/gpt4all-j",
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"gpt-neox-20b": "EleutherAI/gpt-neox-20b",
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"pythia-12b": "EleutherAI/pythia-12b",
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"oasst-sft-4-pythia-12b-epoch-3.5": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
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"dolly-v2-12b": "databricks/dolly-v2-12b",
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"stablelm-tuned-alpha-7b": "stabilityai/stablelm-tuned-alpha-7b",
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"Llama-2-13b-hf": "meta-llama/Llama-2-13b-hf",
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"Llama-2-70b-hf": "meta-llama/Llama-2-70b-hf",
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"open_llama_13b": "openlm-research/open_llama_13b",
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"vicuna-13b-v1.3": "lmsys/vicuna-13b-v1.3",
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"koala": "young-geng/koala",
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"mpt-7b": "mosaicml/mpt-7b",
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"mpt-7b-storywriter": "mosaicml/mpt-7b-storywriter",
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"mpt-30b": "mosaicml/mpt-30b",
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"opt-66b": "facebook/opt-66b",
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"opt-iml-max-30b": "facebook/opt-iml-max-30b",
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"Qwen-7B": "Qwen/Qwen-7B",
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"Qwen-14B": "Qwen/Qwen-14B",
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"Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
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"Qwen-14B-Chat": "Qwen/Qwen-14B-Chat",
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"agentlm-7b": "THUDM/agentlm-7b",
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"agentlm-13b": "THUDM/agentlm-13b",
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"agentlm-70b": "THUDM/agentlm-70b",
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}
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# 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
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SUPPORT_AGENT_MODEL = [
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"azure-api",
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"openai-api",
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"claude-api",
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"zhipu-api",
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"qwen-api",
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|
|
"Qwen",
|
|||
|
|
"baichuan-api",
|
|||
|
|
"agentlm",
|
|||
|
|
"chatglm3",
|
|||
|
|
"xinghuo-api",
|
|||
|
|
]
|