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import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
def load_model(model_path="./", base_model="mistralai/Mistral-7B-v0.1"):
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(
base_model,
torch_dtype=torch.float16,
device_map="auto"
)
model = PeftModel.from_pretrained(model, model_path)
model.eval()
return model, tokenizer
def generate_answer(context, question, model, tokenizer):
prompt = f"Context: {context}\n\nQ: {question}\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response.split("A:")[-1].strip()
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