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embeddings test Instructor-XL
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- __pycache__/util.cpython-310.pyc +0 -0
- util.py +3 -1
Code.txt
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__pycache__/util.cpython-310.pyc
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util.py
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@@ -1,6 +1,7 @@
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import os
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import streamlit as st
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_community.vectorstores import Chroma
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# from langchain.llms.huggingface_pipeline import HuggingFacePipeline
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# from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
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@@ -21,7 +22,8 @@ genai.configure(api_key = os.environ['GOOGLE_API_KEY'])
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model_kwargs = {'device': 'cpu'}
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embeddings = HuggingFaceEmbeddings(model_kwargs=model_kwargs)
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# tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
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# model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", device_map='auto', quantization_config = quantization_config)
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import os
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import streamlit as st
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from sentence_transformers import SentenceTransformer
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from langchain_community.vectorstores import Chroma
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# from langchain.llms.huggingface_pipeline import HuggingFacePipeline
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# from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
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model_kwargs = {'device': 'cpu'}
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embeddings = HuggingFaceEmbeddings(model_name="hkunlp/instructor-xl",model_kwargs=model_kwargs, )
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# embeddings = SentenceTransformer(model_name_or_path="All-MiniLM-L6-v2")
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# tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
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# model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", device_map='auto', quantization_config = quantization_config)
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