Feature Extraction
Transformers
Safetensors
English
Chinese
minicpmv
histopathology
multimodal
spatial-transcriptomics
custom_code
Instructions to use openbmb/SciCore-Omics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/SciCore-Omics with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="openbmb/SciCore-Omics", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/SciCore-Omics", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| language: | |
| - en | |
| - zh | |
| library_name: transformers | |
| tags: | |
| - histopathology | |
| - multimodal | |
| - spatial-transcriptomics | |
| <div align="center"> | |
| # 𧬠SciCore-Omics | |
| ### A tri-modal foundation model unifying histology, spatial transcriptomics, and biological language | |
| [](https://huggingface.co/openbmb/SciCore-Omics) | |
| [](https://github.com/OpenBMB/Scicore-Omics) | |
| [](https://huggingface.co/spaces/Alkaidxxy/SciCore-Omics) | |
| [](https://github.com/OpenBMB/Scicore-Omics/blob/main/LICENSE) | |
| </div> | |
| <p align="center"> | |
| <img src="https://raw.githubusercontent.com/OpenBMB/Scicore-Omics/main/figs/fig1.png" width="95%" alt="SciCore-Omics overview"> | |
| </p> | |
| --- | |
| ## π Overview | |
| **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: | |
| * πΌοΈ image-only reasoning; | |
| * 𧬠gene-only reasoning; | |
| * πΌοΈπ§¬ joint image-gene reasoning; | |
| * π¬ natural-language biomedical interpretation. | |
| --- | |
| ## β¨ Highlights | |
| * Tri-modal modeling of histology, spatial transcriptomics, and language | |
| * Gene-aware transcriptomic encoding with NicheFormer | |
| * Unified image-gene-text reasoning in the language-model space | |
| * Designed for spatial biology, pathology reasoning, and biomedical interpretation | |
| * Open-source model weights, code, and demo | |
| --- | |
| ## π Quick Start | |
| 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.git | |
| cd Scicore-Omics | |
| ``` | |
| Download the model weights: | |
| ```bash | |
| huggingface-cli download openbmb/SciCore-Omics \ | |
| --local-dir ./weights/SciCore-Omics | |
| ``` | |
| Minimal loading example: | |
| ```python | |
| import torch | |
| from transformers import AutoModel, AutoTokenizer, AutoProcessor | |
| model_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 | |
| --- | |
| ## π¦ Resources | |
| | 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 | | |
| --- | |
| ## β οΈ Limitations | |
| 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. | |
| --- | |
| ## π Citation | |
| ```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} | |
| } | |
| ``` | |
| --- | |
| ## π License | |
| This project is released under the Apache-2.0 License. |