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--- |
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license: apache-2.0 |
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language: |
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- ko |
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tags: |
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- kaidol |
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- ai-idol |
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- character-ai |
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- kto |
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- conversational |
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base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503 |
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--- |
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# KAIdol ์ด์งํ KTO |
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KAIdol ์ด์งํ ์บ๋ฆญํฐ KTO ๋ชจ๋ธ (์์ ๋จ, ESTP) |
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## Model Description |
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KAIdol ํ๋ก์ ํธ์ AI ์์ด๋ ์บ๋ฆญํฐ ๋ชจ๋ธ์
๋๋ค. |
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KTO (Kahneman-Tversky Optimization) ๋ฐฉ๋ฒ๋ก ์ผ๋ก ์บ๋ฆญํฐ ์ผ๊ด์ฑ์ ๊ฐํํ์ต๋๋ค. |
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### ์บ๋ฆญํฐ ์ ๋ณด |
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- **์ด๋ฆ**: ์ด์งํ |
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- **์ฑ๊ฒฉ**: ์์ ๋จ (ESTP) |
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- **ํน์ฑ**: ์์ ์ ์ด๊ณ ๋ฐ๋ปํจ, ์ ๊ทน์ ํํ |
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- **๋งํฌ**: ํ๋ฐํ๊ณ ์ง์ ์ ์ธ ๋งํฌ |
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## Training |
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- **Base Model**: Mistral-Small-3.1-24B-Instruct-2503 |
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- **Method**: KTO (Kahneman-Tversky Optimization) |
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- **Framework**: TRL (Transformers Reinforcement Learning) |
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- **Data**: LLM-as-Judge (RLAIF) ๊ธฐ๋ฐ ํ๊ฐ ๋ฐ์ดํฐ |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("developer-lunark/jihu-kto") |
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tokenizer = AutoTokenizer.from_pretrained("developer-lunark/jihu-kto") |
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messages = [ |
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{"role": "system", "content": "๋น์ ์ KAIdol์ AI ์์ด๋ '์ด์งํ'์
๋๋ค."}, |
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{"role": "user", "content": "์ค๋ ๊ธฐ๋ถ ์ด๋?"} |
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] |
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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inputs = tokenizer(text, return_tensors="pt") |
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output = model.generate(**inputs, max_new_tokens=200) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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``` |
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## License |
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Apache 2.0 |
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