Update app.py

app.py

@@ -1,4 +1,119 @@
+import os
+import torch
+from transformers import (
+  AutoTokenizer,
+  AutoModelForCausalLM,
+  BitsAndBytesConfig,
+  pipeline
+)
 
+from transformers import BitsAndBytesConfig
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+
+from langchain.prompts import PromptTemplate
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.llms import HuggingFacePipeline
+from langchain.chains import LLMChain
+import transformers
+model_name='mistralai/Mistral-7B-Instruct-v0.1'
+
+model_config = transformers.AutoConfig.from_pretrained(
+    model_name,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+#################################################################
+# bitsandbytes parameters
+#################################################################
+
+# Activate 4-bit precision base model loading
+use_4bit = True
+
+# Compute dtype for 4-bit base models
+bnb_4bit_compute_dtype = "float16"
+
+# Quantization type (fp4 or nf4)
+bnb_4bit_quant_type = "nf4"
+
+# Activate nested quantization for 4-bit base models (double quantization)
+use_nested_quant = False
+#################################################################
+# Set up quantization config
+#################################################################
+compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=use_4bit,
+    bnb_4bit_quant_type=bnb_4bit_quant_type,
+    bnb_4bit_compute_dtype=compute_dtype,
+    bnb_4bit_use_double_quant=use_nested_quant,
+)
+#############################################################
+# Load pre-trained config
+#################################################################
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+)
+# Connect query to FAISS index using a retriever
+retriever = db.as_retriever(
+    search_type="mmr",
+    search_kwargs={'k': 1}
+)
+from langchain.llms import HuggingFacePipeline
+from langchain.prompts import PromptTemplate
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+
+text_generation_pipeline = transformers.pipeline(
+    model=model,
+    tokenizer=tokenizer,
+    task="text-generation",
+
+    temperature=0.02,
+    repetition_penalty=1.1,
+    return_full_text=True,
+    max_new_tokens=512,
+)
+
+prompt_template = """
+### [INST]
+Instruction: You are a Q&A assistant. Your goal is to answer questions as accurately as possible based on the instructions and context provided without using prior knowledge.You answer in FRENCH
+        Analyse carefully the context and provide a direct answer based on the context.
+Answer in french only
+{context}
+Vous devez répondre aux questions en français.
+
+### QUESTION:
+{question}
+[/INST]
+Answer in french only
+ Vous devez répondre aux questions en français.
+
+ """
+
+mistral_llm = HuggingFacePipeline(pipeline=text_generation_pipeline)
+
+# Create prompt from prompt template
+prompt = PromptTemplate(
+    input_variables=["question"],
+    template=prompt_template,
+)
+
+# Create llm chain
+llm_chain = LLMChain(llm=mistral_llm, prompt=prompt)
+from langchain.chains import RetrievalQA
+
+
+retriever.search_kwargs = {'k':1}
+qa = RetrievalQA.from_chain_type(
+    llm=mistral_llm,
+    chain_type="stuff",
+    retriever=retriever,
+    chain_type_kwargs={"prompt": prompt},
+)
 
 import gradio as gr
 def qna_chatbot(message, history):