Key features
- Unified understanding and generation across 7 modalities through one
natural-language interface.
- Seven modalities, one 8B backbone (protein / RNA / DNA / molecule /
weather / medical-image / text) via a modality router.
- Native scientific encoders/decoders (ESM-2, RNA/DNA ConvFormers, molecular
graph encoder, Swin-ViT weather tower, SAM-based image path) preserve domain
structure a generic tokenizer would destroy.
Capabilities
Table with columns: Modality, Understanding, Generation| Modality | Understanding | Generation |
|---|
| Protein | ✅ | — |
| RNA | ✅ | ✅ |
| DNA | ✅ | — |
| Molecule | ✅ | ✅ |
| Weather | — | ✅ |
| Medical image | — | ✅ |
| Text | ✅ | ✅ |
Understanding = classification / regression / scientific QA. Generation: RNA sequence design · Molecule text → SMILES · Weather 10-day global ERA5 0.25° forecast · Medical-image text-prompted segmentation (SAM 3-based; Meta SAM License).
Benchmarks
神珍 (8B backbone, ~11B total) vs Biology-Instructions (Llama-3.1-8B,
text-token, no scientific encoders) and Intern-S1-Pro (~1T MoE scientific
model). Bold = best; underline = second-best.
Biological sequence understanding
Table with columns: Task, Metric, 神珍 (~11B), Biology-Instructions (8B), Intern-S1-Pro (~1T)| Task | Metric | 神珍 (~11B) | Biology-Instructions (8B) | Intern-S1-Pro (~1T) |
|---|
| DNA · Epigenetic marks (EMP) | MCC | 71.99 | 3.64 | 14.02 |
| DNA · Promoter det. 300bp (PD300) | MCC | 91.17 | 58.18 | 82.65 |
| DNA · Core-promoter (CPD) | MCC | 66.35 | 44.54 |
Aggregate over 20 biological-understanding benchmarks: 神珍 matches or beats the ~1T Intern-S1-Pro on 10/20 and the same-scale 8B text-token baseline on 16/20.
Molecule understanding (SMolInstruct)
Table with columns: Task, Metric, 神珍 (~11B), LlaSMol| Task | Metric | 神珍 (~11B) | LlaSMol |
|---|
| BBBP | Acc | 96.95 | 74.60 |
| HIV | Acc | 97.00 | 96.70 |
| SIDER | Acc | 71.00 | 70.70 |
| ClinTox | Acc | 92.36 | |
Earth-science forecasting — vs ECMWF HRES (day-10, global ERA5 0.25°)
Table with columns: Variable, Metric, 神珍 (~11B), ECMWF HRES (NWP)| Variable | Metric | 神珍 (~11B) | ECMWF HRES (NWP) |
|---|
| Z500 | RMSE ↓ | ≈740 | ≈810 |
| T2M | RMSE ↓ (K) | ≈2.65 | ≈2.90 |
| MSL | RMSE ↓ (Pa) | ≈680 | ≈745 |
神珍 tracks or beats the operational physics-based HRES system, with the advantage growing at longer lead times.
Medical-image segmentation
Mean Dice (%) on the BiomedParse test splits, 102,855 image–prompt pairs across
nine imaging modalities, versus six modality-native segmentation specialists.
Table with columns: Modality, # Samples, 神珍, BiomedParse, MedSAM, SAM, SAM3, DINO+MedSAM, DINO+SAM| Modality | # Samples | 神珍 | BiomedParse | MedSAM | SAM | SAM3 | DINO+MedSAM | DINO+SAM |
|---|
| All | 102,855 | 91.20 | 90.73 | 83.55 | 71.29 | 35.40 | 15.37 | 15.10 |
| CT | 45,306 |
Best overall Dice (All), and best on CT, MRI, pathology, dermoscopy, and endoscopy; on X-ray, Fundus, and Ultrasound the gap to BiomedParse is ≤ 0.5 Dice, and on the smallest split (OCT) it is 1.3.
Usage
Runs via the accompanying code repository (custom multimodal architecture).
git clone https://github.com/Shanghai-Academy-of-AI-For-Science/MKB && cd MKB
pip install -r requirements.txt # Python 3.10; transformers==5.0.0
hf download sais-org/MKB --local-dir ./model
export PYTHONPATH=$PWD/code
python code/inference.py --model_path model --greedy --max_new_tokens 64 \
--rna "GGATGCGATCATGTCTGCACTAACACACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGGGAGTAGTACTAGGATGGGCGACCCCTTAGGAAGTACTCGTGTTGCATCCC" \
--system "You are a non-coding RNA family classifier. Output only the family name, no other text." \
--prompt $'<rna>\nWhich family does this non-coding RNA sequence belong to?'
All weights are contained in model.safetensors: the scientific
encoders/decoders (ESM-2, the Suiren molecular graph encoder, the RNA/DNA
ConvFormers, the Swin-ViT weather tower) and the fine-tuned SAM 3 branch used
for medical-image segmentation.
Each task has a specific --system prompt that fixes the output format; see
run_examples.sh in the repository for per-task examples, weather, and segmentation.
License
Composite license. 神珍's own components — the code, and all weights
except the SAM 3 branch — are Apache-2.0, built on Qwen3-VL (Apache-2.0) and
including merged ESM-2 (MIT) and Polaris/Suiren-derived encoders.
The medical-image segmentation branch embeds SAM 3 weights, which are
governed by Meta's SAM License (SAM_LICENSE.txt, shipped alongside these
weights). SAM 3 use is subject to that license, including its acceptable-use
restrictions (no military / weapons / illegal uses; Trade-Control compliance).
See THIRD_PARTY_LICENSES.md / NOTICE for the full third-party breakdown.
Citation
@misc{mkb2026,
title = {MonkeyKing Bang: A Unified Scientific Multimodal Foundation Model},
author = {Hesen Chen and Xinyu Su and Xiaomeng Yang and Yuetan Lin and Zixiong Yang and Zhiyu Tan and Hao Li},
year = {2026},
note = {https://huggingface.co/sais-org/MKB}
}