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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from peft import PeftModel
import torch
ADAPTER_PATH = "adapter"
BASE_MODEL = "Qwen/Qwen2-0.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.float16
)
model = PeftModel.from_pretrained(model, ADAPTER_PATH)
model.eval()
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
def generate_response(prompt: str) -> str:
formatted = f"<|im_start|>system\nYou are a helpful AI assistant.<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(formatted, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.7,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
decoded = tokenizer.decode(output[0], skip_special_tokens=True)
answer = decoded.split("<|im_start|>assistant\n")[-1].strip()
return answer |