Korean medical language model built with VocMoE (DVE + MoDE). This is the base (continual-pretrained base) checkpoint of MediKo-1.1B-A0.6B.

Spec	Value
Base	Qwen3-0.6B
Total / Active params	1.13B / 0.59B
Medical experts (N) / top-K	8 / 2
Layers	28
Vocab (DVE-expanded)	156,669
Variant	continual-pretrained base

This checkpoint (zero-shot avg): KorMedMCQA 30.9 · KMMLU-Med 30.4 · EnMed 42.6 · KoBEST 50.9 · HAE-RAE 32.6

How it works

VocMoE combines two components:

DVE (Domain Vocabulary Expansion) adds 5,000 Korean medical tokens (from KM-BERT, scored by fragmentation × frequency × PMI), cutting medical-term over-fragmentation ~64%.
MoDE (Mixture-of-Domain-Experts) uses the vocabulary partition as the routing key: medical tokens (id ≥ original_vocab_size) go to dedicated experts, general tokens keep the preserved dense FFN. The general path stays architecturally isolated, preventing catastrophic forgetting at the architectural level.

Family & benchmarks

Model	Base	Total	Active
`MediKo-1.1B-A0.6B`	Qwen3-0.6B	1.13B	0.59B
`MediKo-4B-A1B`	Qwen3-1.7B	3.49B	1.69B
`MediKo-9B-A4B`	Qwen3-4B	9.4B	4.5B
`MediKo-30B-A8B` (not released)	Qwen3-8B	~30B	~8.1B

Benchmark (zero-shot avg, instruction-tuned variants):

Model	Active	KorMedMCQA	KMMLU-Med	EnMed	KoBEST	HAE-RAE
Qwen3-8B (ref)	8B	42.2	52.4	70.7	64.0	57.7
KULLM3 (ref)	10.7B	42.7	39.1	65.4	67.7	54.0
MediKo-1.1B-A0.6B	0.59B	32.6	33.3	51.1	53.4	43.1
MediKo-4B-A1B	1.69B	44.8	44.5	60.8	59.3	50.2
MediKo-9B-A4B	4.5B	57.5	53.0	66.9	63.9	62.1

Table shows the -inst variants. This -base checkpoint's own scores are in the spec line above.

Usage

⚠️ Custom architecture (MediKoForCausalLM) — pass trust_remote_code=True. Token-type routing is generated inside forward() from input_ids.

This is the base (CPT) checkpoint — domain-adapted but not instruction-tuned. For chat / instruction following, use the -inst variant. Use this for completion or as a starting point for your own SFT.

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "MediKo/MediKo-1.1B-A0.6B-base"
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True,
    torch_dtype=torch.bfloat16, device_map="auto",
)
ids = tok("당뇨병의 진단 기준은", return_tensors="pt").to(model.device)
out = model.generate(**ids, max_new_tokens=128)
print(tok.decode(out[0], skip_special_tokens=True))

Intended use & limitations

Research/education for Korean medical NLP.

⚠️ Medical disclaimer. Not a medical device; research/informational use only. Outputs must not be used for diagnosis, treatment, prescription, or clinical decision-making. Always consult a qualified professional. May hallucinate; do not input personally identifiable patient data.

Limitations. Validated only on Korean medical text. MoE → total memory larger than a comparable dense model. CPT token budget (~19.5B) modest vs large English medical LLMs.

Training data

Medical (Korean) ~108 GB + General (Korean) ~39 GB → ~19.5B tokens (≈76% / 24%).
Pipeline: DVE → Qwen3→MediKo dense → MoE upcycle → CPT.

License

Apache-2.0 (inherits Qwen3 base license). Comply with applicable medical / privacy regulations in your jurisdiction.

Citation

Accepted to Findings of the Association for Computational Linguistics: EMNLP 2026.

bibtex

@inproceedings{cho2026vocmoe,
  title     = {VocMoE: Vocabulary-Guided Mixture-of-Experts for Korean Medical Language Models},
  author    = {Cho, Sangyeon and Han, Jaeho and Jeon, Mingyu and Kim, Junyeong},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2026},
  year      = {2026},
}