Add auto-generated model card from override dicts

README.md

@@ -1,62 +1,106 @@
 ---
-datasets: lerobot/pusht
+datasets:
+- lerobot/pusht
+language: en
 library_name: lerobot
 license: apache-2.0
-model_name: eqm
-pipeline_tag: robotics
 tags:
 - robotics
+- imitation-learning
 - eqm
-- lerobot
+- mujoco
+- pytorch_model_hub_mixin
 ---
 
-# Model Card for eqm
+# EQM Policy — eqm_pusht_seed3
 
-<!-- Provide a quick summary of what the model is/does. -->
+Trained with [LeRobot](https://github.com/huggingface/lerobot).  
+Date: `2026-08-22 19:29`  
+Policy type: `eqm` | Device: `cuda`
 
+---
 
-_Model type not recognized — please update this template._
-
+## 📦 Dataset
 
-This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
-See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
+| Parameter | Value |
+|---|---|
+| `dataset.repo_id` | `lerobot/pusht` |
 
 ---
 
-## How to Get Started with the Model
+## 🏋️ Training Config
 
-For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
-Below is the short version on how to train and run inference/eval:
+| Parameter | Value |
+|---|---|
+| `steps` | `20000` |
+| `batch_size` | `8` |
+| `eval_freq` | `0` |
+| `save_freq` | `10000` |
+| `num_workers` | `4` |
+| `seed` | `3` |
+| `eval.n_episodes` | `1` |
+| `eval.batch_size` | `1` |
+| `eval.use_async_envs` | `True` |
 
-### Train from scratch
+---
 
-```bash
-lerobot-train \
-  --dataset.repo_id=${HF_USER}/<dataset> \
-  --policy.type=act \
-  --output_dir=outputs/train/<desired_policy_repo_id> \
-  --job_name=lerobot_training \
-  --policy.device=cuda \
-  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
-  --wandb.enable=true
-```
+## 📐 Policy Architecture
+
+| Parameter | Value |
+|---|---|
+| `use_context_aware_encoder` | `True` |
+| `context_aware_encoder_name` | `facebook/dinov2-with-registers-small` |
+| `context_aware_embodied_pooling` | `spatial_softmax` |
+| `context_aware_num_register_tokens` | `4` |
+| `context_aware_freeze_backbone` | `False` |
+| `resize_shape` | `(224, 224)` |
+| `spatial_softmax_num_keypoints` | `32` |
+| `enable_world_model` | `True` |
+| `jepa_encoder_name` | `facebook/vjepa2-vitl-fpc64-256` |
+| `freeze_jepa_encoder` | `True` |
+| `jepa_tubelet_size` | `2` |
+| `num_video_frames` | `8` |
+| `predictor_depth` | `12` |
+| `predictor_num_heads` | `8` |
+| `predictor_mlp_ratio` | `4.0` |
+| `num_action_tokens_per_timestep` | `4` |
+| `world_model_loss_weight` | `0.6` |
+| `num_predictor_views` | `2` |
 
-_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
+---
 
-### Evaluate the policy/run inference
+## 🎯 Eval Config
+
+| Parameter | Value |
+|---|---|
+| `env.type` | `pusht` |
+| `env.task` | `PushT-v0` |
+| `eval.n_episodes` | `100` |
+| `eval.batch_size` | `4` |
+| `eval.use_async_envs` | `False` |
+| `policy.path` | `/kaggle/working/outputs/train/pusht_seed3/checkpoints/last/pretrained_model` |
+
+---
 
-```bash
-lerobot-record \
-  --robot.type=so100_follower \
-  --dataset.repo_id=<hf_user>/eval_<dataset> \
-  --policy.path=<hf_user>/<desired_policy_repo_id> \
-  --episodes=10
-```
+## 📊 Eval Results
 
-Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
+| Metric | Value |
+|---|---|
+| Episodes | `100` |
+| Success rate | `1.0%` |
+| Avg sum reward | `50.40` |
+| Avg max reward | `0.45` |
+| Eval time (s) | `521.8` |
 
 ---
 
-## Model Details
+## Citation
 
-- **License:** apache-2.0
+```bibtex
+@misc{cadene2024lerobot,
+  author = {Cadene, Remi and Alibert, Simon and others},
+  title  = {LeRobot},
+  year   = {2024},
+  url    = {https://github.com/huggingface/lerobot}
+}
+```