transformers safetensors llama text-generation conversational en ko dataset:PrompTart/PTT_advanced_en_ko arxiv:2410.00683 base_model:beomi/Llama-3-KoEn-8B-Instruct-preview base_model:finetune:beomi/Llama-3-KoEn-8B-Instruct-preview autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model:
- beomi/Llama-3-KoEn-8B-Instruct-preview
- meta-llama/Meta-Llama-3-8B
datasets:
- PrompTart/PTT_advanced_en_ko
language:
- en
- ko
library_name: transformers
---

# Llama-3-KoEn-8B-Instruct-preview Fine-Tuned on Parenthetical Terminology Translation (PTT) Dataset

## Model Overview

This is a **Llama-3-KoEn-8B-Instruct-preview** model fine-tuned on the [**Parenthetical Terminology Translation (PTT)**](https://arxiv.org/abs/2410.00683) dataset. [The PTT dataset](https://huggingface.co/datasets/PrompTart/PTT_advanced_en_ko) focuses on translating technical terms accurately by placing the original English term in parentheses alongside its Korean translation, enhancing clarity and precision in specialized fields. This fine-tuned model is optimized for handling technical terminology in the **Artificial Intelligence (AI)** domain.


## Example Usage

Here’s how to use this fine-tuned model with the Hugging Face `transformers` library:

```python
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load Model and Tokenizer
model_name = "PrompTartLAB/Llama3ko_8B_inst_PTT_enko"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example sentence
text = "The model was fine-tuned using knowledge distillation techniques. The training dataset was created using a collaborative multi-agent framework powered by large language models."
prompt = f"Translate input sentence to Korean \n### Input: {text} \n### Translated:"

# Tokenize and generate translation
input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**input_ids, max_new_tokens=1024)
out_message = tokenizer.decode(outputs[0][len(input_ids["input_ids"][0]):], skip_special_tokens=True)

# " 이 모델은 지식 증류 기법(knowledge distillation techniques)을 사용하여 미세 조정되었습니다. 훈련 데이터셋은 대형 언어 모델(large language models)로 구동되는 협력적 다중 에이전트 프레임워크(collaborative multi-agent framework)를 사용하여 생성되었습니다."

```

## Limitations

- **Out-of-Domain Accuracy**: While the model generalizes to some extent, accuracy may vary in domains that were not part of the training set.
- **Incomplete Parenthetical Annotation**: Not all technical terms are consistently displayed in parentheses; in some cases, terms may be omitted or not annotated as expected.

## Citation

If you use this model in your research, please cite the original dataset and paper:

```tex
@misc{myung2024efficienttechnicaltermtranslation,
      title={Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation}, 
      author={Jiyoon Myung and Jihyeon Park and Jungki Son and Kyungro Lee and Joohyung Han},
      year={2024},
      eprint={2410.00683},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.00683}, 
}
```

## Contact

For questions or feedback, please contact [aeolian83@gmail.com](mailto:aeolian83@gmail.com).