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deploy 3
Browse files- api/question_answering/qa_model.py +5 -6
- requirements.txt +0 -1
api/question_answering/qa_model.py
CHANGED
@@ -13,12 +13,11 @@ from langchain.llms import HuggingFacePipeline
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from langchain.llms.base import LLM
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from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceHubEmbeddings, HuggingFaceInstructEmbeddings
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from langchain.vectorstores import FAISS
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from llama_cpp import Llama
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from api.logger import logger
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from api.question_answering.response import Response
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-
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class LocalBinaryModel(LLM):
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model_id: str = None
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llm: Llama = None
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@@ -49,7 +48,7 @@ class LocalBinaryModel(LLM):
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@property
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def _llm_type(self) -> str:
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return self.model_id
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-
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class TransformersPipelineModel(LLM):
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model_id: str = None
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@@ -170,9 +169,9 @@ class QAModel():
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if 'local_models/' in llm_model_id:
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logger.info('using local binary model')
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self.llm_model = LocalBinaryModel(
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)
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elif 'api_models/' in llm_model_id:
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logger.info('using api served model')
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self.llm_model = APIServedModel(
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from langchain.llms.base import LLM
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from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceHubEmbeddings, HuggingFaceInstructEmbeddings
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from langchain.vectorstores import FAISS
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from api.logger import logger
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from api.question_answering.response import Response
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+
"""
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class LocalBinaryModel(LLM):
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model_id: str = None
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llm: Llama = None
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@property
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def _llm_type(self) -> str:
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return self.model_id
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"""
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class TransformersPipelineModel(LLM):
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model_id: str = None
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if 'local_models/' in llm_model_id:
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logger.info('using local binary model')
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#self.llm_model = LocalBinaryModel(
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# model_id=llm_model_id
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#)
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elif 'api_models/' in llm_model_id:
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logger.info('using api served model')
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self.llm_model = APIServedModel(
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requirements.txt
CHANGED
@@ -7,7 +7,6 @@ discord.py==2.2.2
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evaluate==0.4.0
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fastapi==0.98.0
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langchain==0.0.154
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llama_cpp_python==0.1.39
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nltk==3.8.1
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nbconvert==7.6.0
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nbformat==5.9.0
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evaluate==0.4.0
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fastapi==0.98.0
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langchain==0.0.154
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nltk==3.8.1
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nbconvert==7.6.0
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nbformat==5.9.0
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