Image 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("image-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
- Xet hash:
- 55fa931f7defc88ff5b77fb6056e1bd74bfc42441c5fb2fd1f19ff7c9f69a32e
- Size of remote file:
- 163 kB
- SHA256:
- 66c5a5fd73467da2cc2b7cb1cbca150243a319ceb8272447c276bb21c7b3fa53
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.