Add model card: contents, download, usage, citation

README.md

@@ -0,0 +1,78 @@
+---
+license: mit
+library_name: pytorch
+pipeline_tag: robotics
+tags:
+- world-models
+- jepa
+- planning
+- robotics
+datasets:
+- jepa-world-models/h-jepa
+---
+
+# H-JEPA checkpoints
+
+[![arXiv](https://img.shields.io/badge/arXiv-2610.06805-b5212f?logo=arxiv)](https://arxiv.org/abs/2610.06805)
+[![Website](https://img.shields.io/badge/Website-h--jepa.com-blue)](https://h-jepa.com/)
+[![Code](https://img.shields.io/badge/GitHub-kevinghst/H--JEPA-black?logo=github)](https://github.com/kevinghst/H-JEPA)
+[![Dataset](https://img.shields.io/badge/🤗%20Dataset-jepa--world--models/h--jepa-ffc107)](https://huggingface.co/datasets/jepa-world-models/h-jepa)
+
+Trained models of **H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning**
+(Wancong Zhang\*, Basile Terver\*, Mike Rabbat, Yann LeCun†, Randall Balestriero†). The
+[code release](https://github.com/kevinghst/H-JEPA) trains, evaluates and reproduces the paper's
+planning results with them.
+
+## Contents
+
+| Folder | Models | Seeds | Checkpoints | Size |
+|---|---|---|---|---|
+| `ant/` (Visual AntMaze) | LeWM, H-JEPA 2/3/4 levels, HWM 2/3/4 levels | 42, 43, 44 | 21 | 2.6 GB |
+| `fourroom/` (FourRoom Distractors) | same 7 models | 42, 43, 44 | 21 | 1.7 GB |
+| `cube/` (OGBench Cube) | same 7 models | 42, 43, 44 | 21 | 2.7 GB |
+| `pusht/` (Push-T) | same 7 models | 42, 43, 44 | 21 | 2.7 GB |
+| `droid/` (DROID, 5 fps) | LeWM + IDM, HWM 2 levels, H-JEPA 2 levels | 1, 1000, 10000 | 9 | 2.7 GB |
+
+Each run is a folder `<env>/<env>_<model>/seed<seed>/` with `<env>_<model>_object.ckpt` (the trained
+model) and `normalizer.pt` (action/proprio normalization), the layout the training code writes.
+`<model>` is `lewm`, `hjepa_l<n>` or `hwm_l<n>`, with `<n>` the number of levels. The DROID
+checkpoints are the epoch-100 models.
+
+## Download
+
+Into `$HJEPA_HOME/ckpts`, where the code reads checkpoints:
+
+```bash
+# the 4 simulation environments (84 checkpoints)
+hf download jepa-world-models/h-jepa --local-dir $HJEPA_HOME/ckpts --exclude "droid/*"
+# DROID (9 checkpoints)
+hf download jepa-world-models/h-jepa --local-dir $HJEPA_HOME/ckpts --include "droid/*"
+# one environment or one seed: --include "cube/*", --include "*/seed42/*"
+```
+
+## Usage
+
+The `_object.ckpt` files are pickled `HJEPA` modules, so loading them needs the code release on the
+Python path (run from its `h_jepa/` directory):
+
+```python
+import torch
+
+model = torch.load("cube/cube_hjepa_l3/seed42/cube_hjepa_l3_object.ckpt", weights_only=False)
+```
+
+To reproduce the paper's planning results, pass a checkpoint as `policy=` to `eval.py` or run the
+evaluation scripts, as described in the code release README (simulation: section 4, DROID: section 5).
+The evaluation tasks and datasets are in
+[`jepa-world-models/h-jepa`](https://huggingface.co/datasets/jepa-world-models/h-jepa) (datasets).
+
+## Citation
+
+```bibtex
+@article{zhang2026hjepa,
+  title   = {{H-JEPA}: End-to-End Learning of Hierarchical World Models for Visual Planning},
+  author  = {Zhang, Wancong and Terver, Basile and Rabbat, Mike and LeCun, Yann and Balestriero, Randall},
+  journal = {arXiv preprint arXiv:2610.06805},
+  year    = {2026}
+}
+```