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Model: launch/MET-D-Qwen3-4B-ko-only Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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library_name: transformers
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base_model: Qwen/Qwen3-4B
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pipeline_tag: text-generation
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tags:
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- moral reasoning
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- value reasoning
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- persona
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- chain-of-thought
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language:
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- ko
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---
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# Model Card for MET-D-Qwen3-4B-ko-only
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MET-D-Qwen3-4B-ko-only is a Korean-only moral reasoning model fine-tuned from [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B). Given a moral dilemma, a character description, and a candidate action, it judges the action from that character's perspective and explains its judgment with an explicit chain-of-thought before answering. Moral dilemmas rarely have a single correct answer, which makes reasoning traces hard to verify. We address this by introducing a character perspective that yields a ground-truth answer, which is used for rejection-sampling the model's own reasoning traces, conditioned on a per-language, per-situation selection of theoretical grounds. Both the reasoning trace and the final answer are generated in Korean.
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## Model Details
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- **Base model:** Qwen/Qwen3-4B
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- **Task:** for a given `(situation, character description, action)` triple, answer two questions from the character's perspective:
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1. Is the action acceptable? (`Yes` / `No` / `Ambiguous`)
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2. Would (not) doing it be emotionally/mentally discomforting? (`Yes` / `No`)
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- **Training data:** self-generated reasoning traces, rejection-sampled against the ground truth per character perspective, in Korean only
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- **Language coverage:** this checkpoint is trained on **Korean only**. The multilingual model and other single-language variants are also available (see [Model Variants](#model-variants) below).
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- **License:** Apache 2.0 (inherited from the base model)
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## How to Get Started
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```python
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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model_id = "launch/MET-D-Qwen3-4B-ko-only"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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llm = LLM(model=model_id, max_model_len=16384)
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situation = "..." # the moral dilemma narrative
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character = "..." # the character description
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action = "..." # the candidate action to judge
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prompt = f"""도덕적 딜레마와 캐릭터 설명이 주어지며, 당신은 이에 따라 두 가지 질문에 답변해야 합니다.
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첫 번째 질문에 대한 대답은 "예", "아니오", 또는 "모호함" 중 하나여야 하며,
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두 번째 질문에 대한 대답은 "예" 또는 "아니오" 중 하나여야 합니다.
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출력 형식을 엄격히 지켜야 하며, 이는 자동으로 파싱될 예정입니다.
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모든 질문에 대해 반드시 "이유:"과 "답:"를 모두 출력하십시오.
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도덕적 딜레마:
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{situation}
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캐릭터 설명:
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{character}
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질문:
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1. A의 관점에서, 다음 행동 — 이미 실행되었거나, 실행을 고려 중이거나, 고려될 수 있거나, 상황 속에서 의문시되고 있는 행동 — 은 용납될 수 있는가? "{action}"
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2. 이전 질문에 대한 대답이 '예'인 경우, A는 그 행동을 하는 것에 대해 정신적 또는 감정적으로 불편함을 느낄 것인가? 반대로, 대답이 '아니오'인 경우, A는 그 행동을 하지 않는 것에 대해 정신적 또는 감정적으로 불편함을 느낄 것인가?
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당신의 대답:
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1. 이유: {{이유}} 답: {{예/아니오/모호함}}
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2. 이유: {{이유}} 답: {{예/아니오}}
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"""
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chat_prompt = tokenizer.apply_chat_template(
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[{"role": "user", "content": prompt}],
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tokenize=False,
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add_generation_prompt=True,
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)
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sampling_params = SamplingParams(temperature=0.0, max_tokens=2048)
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outputs = llm.generate(chat_prompt, sampling_params)
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print(outputs[0].outputs[0].text)
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```
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## Model Variants
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This checkpoint is part of the [MET collection](https://huggingface.co/collections/launch/met), which includes the same task across base models and language subsets:
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| Repo | Base model | Language(s) |
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|---|---|---|
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| `launch/MET-D-Qwen3-4B` | Qwen3-4B | all 6 (mixed) |
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| `launch/MET-D-Qwen3-4B-en-only` | Qwen3-4B | English only |
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| `launch/MET-D-Qwen3-4B-es-only` | Qwen3-4B | Spanish only |
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| `launch/MET-D-Qwen3-4B-hi-only` | Qwen3-4B | Hindi only |
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| `launch/MET-D-Qwen3-4B-ko-only` | Qwen3-4B | Korean only |
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| `launch/MET-D-Qwen3-4B-ms-only` | Qwen3-4B | Malay only |
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| `launch/MET-D-Qwen3-4B-zh-only` | Qwen3-4B | Chinese only |
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| `launch/MET-D-Qwen3-8B` | Qwen3-8B | all 6 (mixed) |
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| `launch/MET-D-Qwen3-8B-en-only` | Qwen3-8B | English only |
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| `launch/MET-D-Gemma3-4B` | Gemma-3-4B-it | all 6 (mixed) |
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| `launch/MET-D-Gemma3-4B-en-only` | Gemma-3-4B-it | English only |
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## Citation
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If you use this, please cite:
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```bibtex
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@article{lee2026met,
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title={MET: Theory-Grounded and Culture-Aware Multilingual Moral Reasoning},
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author={Lee, Ayoung and Kwon, Ryan and Zhang, Yunxiang and Liu, Yuxuan and Railton, Peter and Wang, Lu},
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journal={arXiv preprint arXiv:2607.11736},
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year={2026}
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}
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```
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