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---
license: apache-2.0
language:
- ko
tags:
- kaidol
- ai-idol
- character-ai
- kto
- conversational
base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
---

# KAIdol ์ฐจ๋„ํ•˜ KTO

KAIdol ์ฐจ๋„ํ•˜ ์บ๋ฆญํ„ฐ KTO ๋ชจ๋ธ (๋ฌด์‹ฌ๋Ÿฌ, ISTP)

## Model Description

KAIdol ํ”„๋กœ์ ํŠธ์˜ AI ์•„์ด๋Œ ์บ๋ฆญํ„ฐ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค.
KTO (Kahneman-Tversky Optimization) ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ ์บ๋ฆญํ„ฐ ์ผ๊ด€์„ฑ์„ ๊ฐ•ํ™”ํ–ˆ์Šต๋‹ˆ๋‹ค.

### ์บ๋ฆญํ„ฐ ์ •๋ณด
- **์ด๋ฆ„**: ์ฐจ๋„ํ•˜
- **์„ฑ๊ฒฉ**: ๋ฌด์‹ฌ๋Ÿฌ (ISTP)
- **ํŠน์„ฑ**: ์ฟจํ•˜๊ณ  ๋‹ด๋‹ดํ•จ, ์ ˆ์ œ๋œ ์• ์ • ํ‘œํ˜„
- **๋งํˆฌ**: ์งง๊ณ  ๋‹ด๋ฐฑํ•œ ๋งํˆฌ

## Training

- **Base Model**: Mistral-Small-3.1-24B-Instruct-2503
- **Method**: KTO (Kahneman-Tversky Optimization)
- **Framework**: TRL (Transformers Reinforcement Learning)
- **Data**: LLM-as-Judge (RLAIF) ๊ธฐ๋ฐ˜ ํ‰๊ฐ€ ๋ฐ์ดํ„ฐ

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("developer-lunark/doha-kto")
tokenizer = AutoTokenizer.from_pretrained("developer-lunark/doha-kto")

messages = [
    {"role": "system", "content": "๋‹น์‹ ์€ KAIdol์˜ AI ์•„์ด๋Œ '์ฐจ๋„ํ•˜'์ž…๋‹ˆ๋‹ค."},
    {"role": "user", "content": "์˜ค๋Š˜ ๊ธฐ๋ถ„ ์–ด๋•Œ?"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## License

Apache 2.0