Add model card + K562 fold-8 enhancer encoder

README.md

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+---
+license: mit
+tags:
+- biology
+- genomics
+- gene-expression
+- enhancer
+- regulatory-genomics
+- dna
+library_name: pytorch
+---
+
+# EPInformer — reproducible pipeline (pretrained checkpoints)
+
+Pretrained checkpoints for the **[EPInformer reproducible pipeline](https://github.com/pinellolab/EPInformer/tree/reproducible_pipeline)** — a from-raw-ENCODE reproduction of [EPInformer](https://github.com/pinellolab/EPInformer) across 6 cell lines (K562, GM12878, H1, HepG2, HUVEC, NHEK).
+
+Two models (defined in `EPInformer/models.py` on the `reproducible_pipeline` branch):
+
+- **`enhancer_predictor_256bp`** — 256 bp enhancer-activity encoder; predicts `log2(0.1 + sqrt(H3K27ac·DNase))` activity from sequence.
+- **`EPInformer_v2`** — gene-expression model (RNA / CAGE) that reuses the frozen encoder as its sequence backbone.
+
+## Results (12-fold leave-chromosome-out, pooled Pearson R)
+
+**Enhancer encoder** (log2 activity):
+
+| | H1 | HepG2 | K562 | HUVEC | NHEK | GM12878 |
+|---|---|---|---|---|---|---|
+| **R** | 0.820 | 0.743 | 0.740 | 0.742 | 0.677 | 0.617 |
+
+**Gene expression** (shipped `f3` = frozen encoder + 3 enhancer features + promoter signal):
+
+| | K562 | GM12878 | HepG2 | HUVEC | NHEK | H1 |
+|---|---|---|---|---|---|---|
+| **RNA** | 0.856 | 0.860 | 0.845 | 0.839 | 0.828 | 0.781 |
+| **CAGE** | 0.867 | 0.890 | — | — | — | — |
+
+CAGE labels exist only for K562/GM12878 (the other four are RNA-only).
+
+## Files
+
+```
+enhancer_encoders/{CELL}/fold_{i}.pt   # 256 bp enhancer-activity encoders, 12 leave-chromosome-out folds per cell
+```
+
+The fold-8 K562 encoder (`enhancer_encoders/K562/fold_8.pt`) is the checkpoint used in the KLF1 enhancer-activity demo notebook.
+
+## Usage
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+# the model class lives on the reproducible_pipeline branch:
+#   git clone -b reproducible_pipeline https://github.com/pinellolab/EPInformer
+from EPInformer.models import enhancer_predictor_256bp
+
+ckpt = hf_hub_download("JiecongLin/EPInformer-reproducible", "enhancer_encoders/K562/fold_8.pt")
+net = enhancer_predictor_256bp()
+net.load_state_dict(torch.load(ckpt, map_location="cpu", weights_only=False)["model_state_dict"])
+net.eval()
+```
+
+See the **[project wiki](https://github.com/pinellolab/EPInformer/wiki)** for the full training and evaluation guide, and the **[`reproducible_pipeline`](https://github.com/pinellolab/EPInformer/tree/reproducible_pipeline)** branch for code.

enhancer_encoders/K562/fold_8.pt

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