Model Card for Model ID
Base Model : "beomi/Llama-3-Open-Ko-8B"
Dataset : "Bingsu/ko_alpaca_data"
LORA를 사용해 파인튜닝한 모델입니다. 하드웨어 메모리가 부족하여 alpaca dataset의 상위 10개의 데이터만 가지고 학습을 진행하였습니다.
Model Details
해당 모델을 사용해보기 위해선 basemodel의 tokenizer와 특정 instruction template을 지켜야 제대로 된 결과가 출력됩니다.
1from peft import PeftModel, PeftConfig
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4BASEMODEL = "beomi/Llama-3-Open-Ko-8B"
5config = PeftConfig.from_pretrained("gamzadole/llama3_Alpaca_Finetune")
6base_model = AutoModelForCausalLM.from_pretrained("beomi/Llama-3-Open-Ko-8B", load_in_4bit=True, device_map="auto")
7model = PeftModel.from_pretrained(base_model, "gamzadole/llama3_Alpaca_Finetune")
8model = model.cuda()
9
10tokenizer = AutoTokenizer.from_pretrained(BASEMODEL)
11tokenizer.pad_token = tokenizer.eos_token
12tokenizer.padding_side = "right"
13
14prompt_input_template = """아래는 작업을 설명하는 지시사항과 추가 정보를 제공하는 입력이 짝으로 구성됩니다. 이에 대한 적절한 응답을 작성해주세요.
15
16### 지시사항:
17{instruction}
18
19### 입력:
20{input}
21
22### 응답:"""
23
24
25prompt_no_input_template = """아래는 작업을 설명하는 지시사항입니다. 이에 대한 적절한 응답을 작성해주세요.
26
27### 지시사항:
28{instruction}
29
30### 응답:"""
31
32def generate_response(prompt, model):
33 encoded_input = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
34 model_inputs = encoded_input.to('cuda')
35
36 generated_ids = model.generate(**model_inputs, max_new_tokens=512, do_sample=True, pad_token_id=tokenizer.eos_token_id)
37
38 decoded_output = tokenizer.batch_decode(generated_ids)
39
40 return decoded_output[0].replace(prompt, "")
41
42instruction = "건강을 유지하기 위한 세 가지 팁을 알려주세요."
43prompt = prompt_no_input_template.format(instruction=instruction)
44
45generate_response(prompt, model)
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