---
base_model: beomi/Llama-3-Open-Ko-8B
library_name: peft
---

# Model Card for Model ID

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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. 


```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

BASEMODEL = "beomi/Llama-3-Open-Ko-8B"
config = PeftConfig.from_pretrained("gamzadole/llama3_Alpaca_Finetune")
base_model = AutoModelForCausalLM.from_pretrained("beomi/Llama-3-Open-Ko-8B", load_in_4bit=True, device_map="auto")
model = PeftModel.from_pretrained(base_model, "gamzadole/llama3_Alpaca_Finetune")
model = model.cuda()

tokenizer = AutoTokenizer.from_pretrained(BASEMODEL)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"

prompt_input_template = """아래는 작업을 설명하는 지시사항과 추가 정보를 제공하는 입력이 짝으로 구성됩니다. 이에 대한 적절한 응답을 작성해주세요.

### 지시사항:
{instruction}

### 입력:
{input}

### 응답:"""


prompt_no_input_template = """아래는 작업을 설명하는 지시사항입니다. 이에 대한 적절한 응답을 작성해주세요.

### 지시사항:
{instruction}

### 응답:"""

def generate_response(prompt, model):
    encoded_input = tokenizer(prompt,  return_tensors="pt", add_special_tokens=True)
    model_inputs = encoded_input.to('cuda')

    generated_ids = model.generate(**model_inputs, max_new_tokens=512, do_sample=True, pad_token_id=tokenizer.eos_token_id)

    decoded_output = tokenizer.batch_decode(generated_ids)

    return decoded_output[0].replace(prompt, "")

instruction = "건강을 유지하기 위한 세 가지 팁을 알려주세요."
prompt = prompt_no_input_template.format(instruction=instruction)

generate_response(prompt, model)
```
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### Framework versions

- PEFT 0.11.1