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

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
Base Model : "beomi/Llama-3-Open-Ko-8B"

Dataset : "Bingsu/ko_alpaca_data"

LORA๋ฅผ ์‚ฌ์šฉํ•ด ํŒŒ์ธํŠœ๋‹ํ•œ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค. ํ•˜๋“œ์›จ์–ด ๋ฉ”๋ชจ๋ฆฌ๊ฐ€ ๋ถ€์กฑํ•˜์—ฌ alpaca dataset์˜ ์ƒ์œ„ 10๊ฐœ์˜ ๋ฐ์ดํ„ฐ๋งŒ ๊ฐ€์ง€๊ณ  ํ•™์Šต์„ ์ง„ํ–‰ํ•˜์˜€์Šต๋‹ˆ๋‹ค.

## Model Details

ํ•ด๋‹น ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•ด๋ณด๊ธฐ ์œ„ํ•ด์„  basemodel์˜ tokenizer์™€ ํŠน์ • instruction template์„ ์ง€์ผœ์•ผ ์ œ๋Œ€๋กœ ๋œ ๊ฒฐ๊ณผ๊ฐ€ ์ถœ๋ ฅ๋ฉ๋‹ˆ๋‹ค. 


```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 = """์•„๋ž˜๋Š” ์ž‘์—…์„ ์„ค๋ช…ํ•˜๋Š” ์ง€์‹œ์‚ฌํ•ญ๊ณผ ์ถ”๊ฐ€ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋Š” ์ž…๋ ฅ์ด ์ง์œผ๋กœ ๊ตฌ์„ฑ๋ฉ๋‹ˆ๋‹ค. ์ด์— ๋Œ€ํ•œ ์ ์ ˆํ•œ ์‘๋‹ต์„ ์ž‘์„ฑํ•ด์ฃผ์„ธ์š”.

### ์ง€์‹œ์‚ฌํ•ญ:
{instruction}

### ์ž…๋ ฅ:
{input}

### ์‘๋‹ต:"""


prompt_no_input_template = """์•„๋ž˜๋Š” ์ž‘์—…์„ ์„ค๋ช…ํ•˜๋Š” ์ง€์‹œ์‚ฌํ•ญ์ž…๋‹ˆ๋‹ค. ์ด์— ๋Œ€ํ•œ ์ ์ ˆํ•œ ์‘๋‹ต์„ ์ž‘์„ฑํ•ด์ฃผ์„ธ์š”.

### ์ง€์‹œ์‚ฌํ•ญ:
{instruction}

### ์‘๋‹ต:"""

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 = "๊ฑด๊ฐ•์„ ์œ ์ง€ํ•˜๊ธฐ ์œ„ํ•œ ์„ธ ๊ฐ€์ง€ ํŒ์„ ์•Œ๋ ค์ฃผ์„ธ์š”."
prompt = prompt_no_input_template.format(instruction=instruction)

generate_response(prompt, model)
```
### Model Description

<!-- Provide a longer summary of what this model is. -->



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## Uses

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## How to Get Started with the Model

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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### Framework versions

- PEFT 0.11.1