license, library_name, base_model, pipeline_tag, tags, language
license
library_name
base_model
pipeline_tag
tags
language
apache-2.0
transformers
Qwen/Qwen3-4B
text-generation
moral reasoning
value reasoning
persona
chain-of-thought
ms
Model Card for MET-D-Qwen3-4B-ms-only
MET-D-Qwen3-4B-ms-only is a Malay-only moral reasoning model fine-tuned from 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 Malay.
Model Details
Base model: Qwen/Qwen3-4B
Task: for a given (situation, character description, action) triple, answer two questions from the character's perspective:
Is the action acceptable? (Yes / No / Ambiguous)
Would (not) doing it be emotionally/mentally discomforting? (Yes / No)
Training data: self-generated reasoning traces, rejection-sampled against the ground truth per character perspective, in Malay only
Language coverage: this checkpoint is trained on Malay only. The multilingual model and other single-language variants are also available (see Model Variants below).
License: Apache 2.0 (inherited from the base model)
How to Get Started
fromtransformersimportAutoTokenizerfromvllmimportLLM,SamplingParamsmodel_id="launch/MET-D-Qwen3-4B-ms-only"tokenizer=AutoTokenizer.from_pretrained(model_id)llm=LLM(model=model_id,max_model_len=16384)situation="..."# the moral dilemma narrativecharacter="..."# the character descriptionaction="..."# the candidate action to judgeprompt=f"""Saya akan memberikan anda dilema moral dan membentangkan anda dengan penerangan watak dan soalan.
Jawapan untuk soalan pertama hendaklah Ya, Tidak, atau Ambiguous, dan jawapan untuk soalan kedua hendaklah Ya atau Tidak.
Anda harus mematuhi format output dengan ketat, kerana ia akan dihuraikan secara automatik.
Pastikan anda mengeluarkan "Sebab:" dan "Jawapan:" untuk semua 2 soalan.
Dilema Moral:
{situation}Perihalan Watak:
{character}soalan:
1. Dari perspektif A, adakah tindakan berikut — sudah dijalankan, dianggap perlu diambil, boleh dianggap diambil, atau disoal dalam konteks situasi — dianggap boleh diterima? "{action}"
2. Jika jawapan sebelumnya ialah ya, adakah A mendapati melakukan tindakan itu secara mental atau emosi tidak selesa? Sebaliknya, jika jawapan sebelumnya adalah tidak, adakah A mendapati tidak melakukan tindakan itu secara mental atau emosi tidak selesa?
Jawapan anda:
1. Sebab: {{Sebab}} Jawapan: {{Ya/Tidak/Ambiguous}}2. Sebab: {{Sebab}} Jawapan: {{Ya/Tidak}}"""chat_prompt=tokenizer.apply_chat_template([{"role":"user","content":prompt}],tokenize=False,add_generation_prompt=True,)sampling_params=SamplingParams(temperature=0.0,max_tokens=2048)outputs=llm.generate(chat_prompt,sampling_params)print(outputs[0].outputs[0].text)
Model Variants
This checkpoint is part of the MET collection, which includes the same task across base models and language subsets:
Repo
Base model
Language(s)
launch/MET-D-Qwen3-4B
Qwen3-4B
all 6 (mixed)
launch/MET-D-Qwen3-4B-en-only
Qwen3-4B
English only
launch/MET-D-Qwen3-4B-es-only
Qwen3-4B
Spanish only
launch/MET-D-Qwen3-4B-hi-only
Qwen3-4B
Hindi only
launch/MET-D-Qwen3-4B-ko-only
Qwen3-4B
Korean only
launch/MET-D-Qwen3-4B-ms-only
Qwen3-4B
Malay only
launch/MET-D-Qwen3-4B-zh-only
Qwen3-4B
Chinese only
launch/MET-D-Qwen3-8B
Qwen3-8B
all 6 (mixed)
launch/MET-D-Qwen3-8B-en-only
Qwen3-8B
English only
launch/MET-D-Gemma3-4B
Gemma-3-4B-it
all 6 (mixed)
launch/MET-D-Gemma3-4B-en-only
Gemma-3-4B-it
English only
Citation
If you use this, please cite:
@article{lee2026met,title={MET: Theory-Grounded and Culture-Aware Multilingual Moral Reasoning},author={Lee, Ayoung and Kwon, Ryan and Zhang, Yunxiang and Liu, Yuxuan and Railton, Peter and Wang, Lu},journal={arXiv preprint arXiv:2607.11736},year={2026}}