File size: 1,080 Bytes
b763ff0 cdf779a b763ff0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 |
import gradio as gr
from auto_gptq import AutoGPTQForCausalLM
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from utils import build_faiss_index, retrieve
# Load documents
with open("documents/1mg_rag.txt") as f:
docs = [line.strip() for line in f if line.strip()]
# Build FAISS index
index, _ = build_faiss_index(docs)
# Load quantized Mistral 7B
model_id = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoGPTQForCausalLM.from_quantized(model_id, device_map="auto", trust_remote_code=True)
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
def answer_question(query):
context = "\n".join(retrieve(query, index, docs))
prompt = f"[INST] Use the following context to answer the question.\n\nContext:\n{context}\n\nQuestion: {query} [/INST]"
result = generator(prompt, max_new_tokens=256, do_sample=True, temperature=0.7)
return result[0]['generated_text']
gr.Interface(fn=answer_question, inputs="text", outputs="text", title="Mistral RAG").launch()
|