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1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3tokenizer = AutoTokenizer.from_pretrained("NAPS-ai/naps-llama-3.1-8b-instruct-v0.4")
4model = AutoModelForCausalLM.from_pretrained("NAPS-ai/naps-llama-3.1-8b-instruct-v0.4")
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import transformers
3import torch
4
5tokenizer = AutoTokenizer.from_pretrained("NAPS-ai/naps-llama-3.1-8b-instruct-v0.4")
6model = AutoModelForCausalLM.from_pretrained("NAPS-ai/naps-llama-3.1-8b-instruct-v0.4")
7
8
9
10
11pipeline = transformers.pipeline(
12 "text-generation",
13 model=model,
14 tokenizer=tokenizer,
15 model_kwargs={"torch_dtype": torch.bfloat16},
16 device=0,
17)
18
19def answering(question):
20 messages = [
21 {"role": "system", "content": "당신은 항상 친절하게 대답하는 안내원입니다."},
22 {"role": "user", "content": question},
23 ]
24 outputs = pipeline(
25 messages,
26 max_new_tokens=1024,
27 pad_token_id = pipeline.tokenizer.eos_token_id
28 )
29 return outputs[0]["generated_text"][2]['content']
30
31
32
33
34while True:
35 question = input("질문을 입력하세요 : ")
36 if question == "종료":
37 print("프로그램 종료")
38 break
39 answer = answering(question)
40 print(f"AI의 답변: {answer}")
41