jihu-kto / README.md
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metadata
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 ์ด์ง€ํ›„ KTO

KAIdol ์ด์ง€ํ›„ ์บ๋ฆญํ„ฐ KTO ๋ชจ๋ธ (์ˆœ์ •๋‚จ, ESTP)

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„: ์ด์ง€ํ›„
  • ์„ฑ๊ฒฉ: ์ˆœ์ •๋‚จ (ESTP)
  • ํŠน์„ฑ: ์ˆœ์ •์ ์ด๊ณ  ๋”ฐ๋œปํ•จ, ์ ๊ทน์  ํ‘œํ˜„
  • ๋งํˆฌ: ํ™œ๋ฐœํ•˜๊ณ  ์ง์ ‘์ ์ธ ๋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

from transformers import AutoModelForCausalLM, AutoTokenizer

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

messages = [
    {"role": "system", "content": "๋‹น์‹ ์€ KAIdol์˜ AI ์•„์ด๋Œ '์ด์ง€ํ›„'์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