Making large AI models cheaper, faster and more accessible
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from colossalqa.prompt.prompt import PROMPT_DISAMBIGUATE_ZH, PROMPT_RETRIEVAL_QA_ZH, SUMMARY_PROMPT_ZH
from colossalqa.text_splitter import ChineseTextSplitter
ALL_CONFIG = {
"embed": {
"embed_name": "m3e", # embedding model name
"embed_model_name_or_path": "moka-ai/m3e-base", # path to embedding model, could be a local path or a huggingface path
"embed_model_device": {"device": "cpu"},
},
"model": {
"mode": "api", # "local" for loading models, "api" for using model api
"model_name": "chatgpt_api", # local model name, "chatgpt_api" or "pangu_api"
"model_path": "", # path to the model, could be a local path or a huggingface path. don't need if using an api
"device": {"device": "cuda"},
},
"splitter": {"name": ChineseTextSplitter},
"retrieval": {"retri_top_k": 3, "retri_kb_file_path": "./", "verbose": True}, # path to store database files
"chain": {
"mem_summary_prompt": SUMMARY_PROMPT_ZH, # summary prompt template
"mem_human_prefix": "用户",
"mem_ai_prefix": "Assistant",
"mem_max_tokens": 2000,
"mem_llm_kwargs": {"max_new_tokens": 50, "temperature": 1, "do_sample": True},
"disambig_prompt": PROMPT_DISAMBIGUATE_ZH, # disambiguate prompt template
"disambig_llm_kwargs": {"max_new_tokens": 30, "temperature": 1, "do_sample": True},
"gen_llm_kwargs": {"max_new_tokens": 100, "temperature": 1, "do_sample": True},
"gen_qa_prompt": PROMPT_RETRIEVAL_QA_ZH, # generation prompt template
"verbose": True,
},
}