PrompTartLAB
/

Llama3ko_8B_inst_PTT_enko

@@ -1,199 +1,76 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+datasets:
+- PrompTart/PTT_advanced_en_ko
+language:
+- en
+- ko
+base_model:
+- beomi/Llama-3-KoEn-8B-Instruct-preview
+- meta-llama/Meta-Llama-3-8B
 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:
+<span style="color:red">*Note:</span> `M2M100Tokenizer` depends on <span style="color:blue">sentencepiece</span>, so make sure to install it before running the example.* To install `sentencepiece`, run `pip install sentencepiece`
+```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(message_0, 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 [jiyoon0424@gmail.com](mailto:jiyoon0424@gmail.com).