Feature Extraction
Transformers
Safetensors
virtual_cell_patient
biology
genomics
single-cell-rna-seq
patient-classification
custom_code
Instructions to use ConvergeBio/virtual-cell-patient with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ConvergeBio/virtual-cell-patient with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ConvergeBio/virtual-cell-patient", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ConvergeBio/virtual-cell-patient", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
initial: weights + modeling code + lean config
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## Installation
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```bash
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pip install -r requirements.txt
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```
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| `config.json` | Architecture config |
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| `gene_names.txt` | Ordered list of 18,301 HGNC gene symbols |
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| `train.py` | Fine-tuning / training script |
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| `model.safetensors` | Pretrained weights |
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## Citation
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## Installation
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`requirements.txt` is included in the repository files. Download it (or clone
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the repo) and install:
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```bash
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pip install -r requirements.txt
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```
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| `config.json` | Architecture config |
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| `gene_names.txt` | Ordered list of 18,301 HGNC gene symbols |
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| `train.py` | Fine-tuning / training script |
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| `requirements.txt` | Python dependencies |
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| `model.safetensors` | Pretrained weights |
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## Citation
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