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---
license: mit
datasets:
- CarrotAI/ko-instruction-dataset
- CarrotAI/Korean-sharegpt-dataset
- CarrotAI/qa-ko-dataset
language:
- ko
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
์ƒ์„ฑํ•œ ํ•œ๊ตญ์–ด ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ axolotl์„ ์ด์šฉํ•˜์—ฌ ํŒŒ์ธํŠœ๋‹ํ•˜์˜€์Šต๋‹ˆ๋‹ค.
### Model Description
Qwen/Qwen2-1.5B-Instruct ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ์ƒ์„ฑํ•˜์˜€์Šต๋‹ˆ๋‹ค.
### LogicKor
default
| Category | Single turn | Multi turn |
|---|---|---|
| ์ถ”๋ก (Reasoning) | 4.14 | 2.29 |
| ์ˆ˜ํ•™(Math) | 2.43 | 1.14 |
| ๊ธ€์“ฐ๊ธฐ(Writing) | 6.43 | 7.86 |
| ์ฝ”๋”ฉ(Coding) | 5.14 | 4.57 |
| ์ดํ•ด(Understanding) | 5.29 | 4.57 |
| ๋ฌธ๋ฒ•(Grammar) | 3.71 | 1.29 |
| Category | Score |
|---|---|
| Single turn | 4.52 |
| Multi turn | 3.62 |
| Overall | 4.07 |
1-shot
| Category | Single turn | Multi turn |
|---|---|---|
| ์ถ”๋ก (Reasoning) | 4.14 | 1.43 |
| ์ˆ˜ํ•™(Math) | 2.86 | 1.00 |
| ๊ธ€์“ฐ๊ธฐ(Writing) | 5.00 | 4.57 |
| ์ฝ”๋”ฉ(Coding) | 3.14 | 3.43 |
| ์ดํ•ด(Understanding) | 4.29 | 3.71 |
| ๋ฌธ๋ฒ•(Grammar) | 2.71 | 1.43 |
| Category | Score |
|---|---|
| Single turn | 3.69 |
| Multi turn | 2.60 |
| Overall | 3.14 |
cot-1-shot
| Category | Single turn | Multi turn |
|---|---|---|
| ์ถ”๋ก (Reasoning) | 3.00 | 2.86 |
| ์ˆ˜ํ•™(Math) | 1.57 | 1.00 |
| ๊ธ€์“ฐ๊ธฐ(Writing) | 5.86 | 6.00 |
| ์ฝ”๋”ฉ(Coding) | 4.29 | 4.14 |
| ์ดํ•ด(Understanding) | 3.43 | 3.43 |
| ๋ฌธ๋ฒ•(Grammar) | 3.00 | 1.14 |
| Category | Score |
|---|---|
| Single turn | 3.52 |
| Multi turn | 3.10 |
| Overall | 3.31 |
### 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.
```
@article{Carrot-Ko-2.1B-Instruct,
title={CarrotAI/Carrot-Ko-2.1B-Instruct Card},
author={CarrotAI (L, GEUN)},
year={2024},
url = {https://huggingface.co./CarrotAI/Carrot-2.1B-Instruct}
}