README.md

---
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"

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)
```
### Model Description

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



- **Developed by:** [More Information Needed]
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### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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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).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Hardware

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## Citation [optional]

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.11.1