openbmb
SciCore-Omics
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
openbmb
Model Tree
Input Modalities
Output Modalities
Supported Functionality
GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
openbmb
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
openbmb
Model Tree
Input Modalities
Output Modalities
Supported Functionality
SciCore-Omics is a tri-modal biomedical foundation model that connects histology images, spatial transcriptomic profiles, and biological language for spatial biology and pathology-related reasoning.
The model introduces a gene-aware branch based on NicheFormer + Gene Q-Former + Gene Projector, enabling transcriptomic information to be aligned with the language-model token space.
SciCore-Omics supports:
This Hugging Face repository hosts the model weights.
For full inference and training code, please refer to the GitHub repository:
bash
git clone https://github.com/OpenBMB/Scicore-Omics.gitcd Scicore-Omics
Download the model weights:
bash
huggingface-cli download openbmb/SciCore-Omics \--local-dir ./weights/SciCore-Omics
Minimal loading example:
python
import torchfrom transformers import AutoModel, AutoTokenizer, AutoProcessormodel_path = "openbmb/SciCore-Omics"processor = AutoProcessor.from_pretrained(model_path,trust_remote_code=True)tokenizer = AutoTokenizer.from_pretrained(model_path,trust_remote_code=True)model = AutoModel.from_pretrained(model_path,trust_remote_code=True,torch_dtype=torch.bfloat16,device_map="auto")model.eval()
For complete examples, please see:
https://github.com/OpenBMB/Scicore-Omics/tree/main/eval
| Resource | Link |
|---|---|
| Model weights | https://huggingface.co/openbmb/SciCore-Omics |
| GitHub code | https://github.com/OpenBMB/Scicore-Omics |
| Online demo | https://huggingface.co/spaces/Alkaidxxy/SciCore-Omics |
SciCore-Omics is released for research use only.
It may generate inaccurate or incomplete biomedical interpretations and should not be used as a standalone clinical diagnostic or treatment recommendation system.
bibtex
@misc{xiao2026scicoreomics,title = {SciCore-Omics: a tri-modal foundation model unifying histology, spatial transcriptomics and language for spatial biology},author = {Xiao, Xinyu and Li, Yunfei and Zeng, Zheni and others},year = {2026},note = {Manuscript in preparation}}
This project is released under the Apache-2.0 License.