---
license: llama3
language:
- ko
- en
pipeline_tag: text-generation
---

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

## Model Details

axolotl를 이용하여 공개/자체적으로 생성된 한국어, 영어 데이터셋으로 파인튜닝하였습니다.

### Model Description

### Socre

#### llm_kr_eval
| 평가 지표 | 점수 |
| --- | --- |
| AVG_llm_ok_eval | 0.4282 |
| EL (Easy Language) | 0.1264 |
| FA (False Alarm) | 0.2184 |
| NLI (Natural Language Understanding) | 0.5767 |
| QA (Question Answering) | 0.5100 |
| RC (Reconstruction) | 0.7096 |
| klue_ner_set_f1 (Klue Named Entity Recognition F1 Score) | 0.1429 |
| klue_re_exact_match (Klue Reference Exact Match) | 0.1100 |
| kmmlu_preview_exact_match (Kmmlu Preview Exact Match) | 0.4400 |
| kobest_copa_exact_match (Kobest COPA Exact Match) | 0.8100 |
| kobest_hs_exact_match (Kobest HS Exact Match) | 0.3800 |
| kobest_sn_exact_match (Kobest SN Exact Match) | 0.9000 |
| kobest_wic_exact_match (Kobest WIC Exact Match) | 0.5800 |
| korea_cg_bleu (Korean CG BLEU) | 0.2184 |
| kornli_exact_match (KornLI Exact Match) | 0.5400 |
| korsts_pearson (KorSTS Pearson Correlation Coefficient) | 0.6225 |
| korsts_spearman (KorSTS Spearman Rank Correlation Coefficient) | 0.6064 |

#### LogicKor
| 카테고리 | 싱글 점수 평균 | 멀티 점수 평균 |
| --- | --- | --- |
| 수학(Math) | 4.43 | 3.71 |
| 이해(Understanding) | 9.29 | 6.86 |
| 추론(Reasoning) | 5.71 | 5.00 |
| 글쓰기(Writing) | 7.86 | 7.43 |
| 코딩(Coding) | 7.86 | 6.86 |
| 문법(Grammar) | 6.86 | 3.86 |
| 전체 싱글 점수 평균 | 7.00 | - |
| 전체 멀티 점수 평균 | - | 5.62 |
| 전체 점수 | - | 6.31 |

### Built with Meta Llama 3
License Llama3 License: https://llama.meta.com/llama3/license

### Applications
This fine-tuned model is particularly suited for [mention applications, e.g., chatbots, question-answering systems, etc.]. Its enhanced capabilities ensure more accurate and contextually appropriate responses in these domains.

### Limitations and Considerations
While our fine-tuning process has optimized the model for specific tasks, it's important to acknowledge potential limitations. The model's performance can still vary based on the complexity of the task and the specificities of the input data. Users are encouraged to evaluate the model thoroughly in their specific context to ensure it meets their requirements.

If you liked this model, please use the card below

```
@article{Llama3KoCarrot8Bit,
  title={CarrotAI/Llama3-Ko-Carrot-8B-it Card},
  author={CarrotAI (L, GEUN)},
  year={2024},
  url = {https://huggingface.co/CarrotAI/Llama3-Ko-Carrot-8B-it/}
}
```