Attached GitHub link and usage code in README

README.md

@@ -10,7 +10,7 @@ tags:
   - world-model
 ---
 
-# WAM-0.1 Dataset 
+# WAM-0.1 Dataset
 
 WAM-0.1 is an image-and-action dataset for learning Panda robot dynamics and world representations.
 
@@ -18,11 +18,15 @@ Created by **Atom Ctrl** and **Aditya Panigrahi**.
 
 Website: https://atomctrl.com
 
+GitHub: https://github.com/adityapp1213/Wam-0.1
+
+Hugging Face: https://huggingface.co/datasets/atomctrl/wam-0.1
+
 ## Dataset overview
 
 The dataset contains two aligned learning levels.
 
-![image](https://cdn-uploads.huggingface.co/production/uploads/68dc1570c7fc381a7bae2aaa/JxfpN9VA5M5vEnCuEKV-6.png)
+![Dataset overview](https://cdn-uploads.huggingface.co/production/uploads/68dc1570c7fc381a7bae2aaa/JxfpN9VA5M5vEnCuEKV-6.png)
 
 ### Level 0
 
@@ -51,22 +55,18 @@ Level 1 samples correspond directly to sequences of Level 0 transitions.
 
 ### Normal trajectories
 
-
 ![Normal trajectory](https://cdn-uploads.huggingface.co/production/uploads/68dc1570c7fc381a7bae2aaa/5NM8_MREYvVHvie2txJo-.png)
 
 ### Randomized trajectories
 
-
 ![Random trajectory](https://cdn-uploads.huggingface.co/production/uploads/68dc1570c7fc381a7bae2aaa/VvZIjkTKYBmxTBo_QuqHF.png)
 
 ### RoboCasa trajectories
 
-
 ![RoboCasa trajectory](https://cdn-uploads.huggingface.co/production/uploads/68dc1570c7fc381a7bae2aaa/R_Kg1TxijlVv9Ona0pqRt.png)
 
 ### Additional examples
 
-
 ![Additional trajectory](https://cdn-uploads.huggingface.co/production/uploads/68dc1570c7fc381a7bae2aaa/FECjQ5dwtG3gs9K2Rh4AW.png)
 
 ## Core tasks
@@ -82,6 +82,8 @@ Level 1 samples correspond directly to sequences of Level 0 transitions.
 - PickPlaceSingle
 - NutAssemblySquare
 
+RoboCasa contributes additional official kitchen and household manipulation tasks.
+
 ## Dataset layout
 
     data/
@@ -95,13 +97,53 @@ Level 1 samples correspond directly to sequences of Level 0 transitions.
             ├── random/
             └── robocasa/
 
+## Google Colab example
+
+This example downloads only one shard instead of the complete dataset.
+
+    !pip -q install huggingface_hub
+
+    from huggingface_hub import hf_hub_download
+
+    shard = hf_hub_download(
+        repo_id="atomctrl/wam-0.1",
+        repo_type="dataset",
+        filename="shards/lift-normal.tar",
+        local_dir="/content/wam-test"
+    )
+
+    print(shard)
+
+Extract the shard:
+
+    import tarfile
+    from pathlib import Path
+
+    extract_dir = Path("/content/wam-test/extracted")
+    extract_dir.mkdir(parents=True, exist_ok=True)
+
+    with tarfile.open(shard, "r") as tar:
+        tar.extractall(extract_dir)
+
+    print("Extracted to:", extract_dir)
+
 ## Intended use
 
 WAM-0.1 is intended for H-JEPA training, image-based world models, action-conditioned prediction, robot dynamics learning, subgoal prediction, and robotics planning research.
 
 ## Download
 
-    hf download atomctrl/wam-0.1 --repo-type dataset --local-dir wam-0.1
+    pip install -U huggingface_hub
+
+    hf download atomctrl/wam-0.1 \
+      --repo-type dataset \
+      --local-dir wam-0.1
+
+## Links
+
+- GitHub code: https://github.com/adityapp1213/Wam-0.1
+- Dataset: https://huggingface.co/datasets/atomctrl/wam-0.1
+- Website: https://atomctrl.com
 
 ## Citation
 
@@ -112,3 +154,4 @@ WAM-0.1 is intended for H-JEPA training, image-based world models, action-condit
       publisher = {Atom Ctrl},
       url       = {https://huggingface.co/datasets/atomctrl/wam-0.1}
     }
+