---
# 1. Metadata Block
license: mit
library_name: pytorch-lightning
pipeline_tag: tabular-classification
tags:
- biology
- genomics
datasets:
- Genentech/human-atac-catlas-data
---

# human-atac-catlas-model

## Model Description
This model is a multi-task binary classifier trained to predict chromatin accessibility across 204 cell types. It was trained by fine-tuning the Enformer model using the `grelu` library on top of the CATlas human enhancer dataset.

- **Architecture:** Fine-tuned Enformer
- **Input:** Genomic sequences (hg38)
- **Output:** Binary accessibility predictions for 204 cell type tasks.

## Repository Content
1. `model.ckpt`: The trained model weights and hyperparameters (PyTorch Lightning checkpoint).
2. `2_train.ipynb`: Jupyter notebook containing the training logic, architecture definition, and evaluation loops.
3. `output.log`: Training logs.

## How to use
To load this model for inference or fine-tuning, use the `grelu` interface:

```python
from grelu.lightning import LightningModel
from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download(
    repo_id="Genentech/human-atac-catlas-model", 
    filename="model.ckpt"
)

model = LightningModel.load_from_checkpoint(ckpt_path)
model.eval()
```