Upload model weights with LFS

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+**/d/* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,39 @@
 ---
+language:
+- en
 license: mit
+library_name: jax
+tags:
+- robotics
+- reinforcement-learning
+- continuous-control
+- test-time-adaptation
+- flow-matching
+- orbax
 ---
+# Model Card for panda_reversible_flow_model
+
+## Model Description
+This repository contains the pre-trained neural network weights for the **Reversible Flow Adaptation** architecture, applied to the **Panda** environment using the **reversible_flow** policy.
+
+These checkpoints represent the fully-converged policy at the final epoch (Epoch 600) across 3 independent random seeds (`seed_1`, `seed_2`, `seed_42`).
+
+### Architecture
+The policy consists of a dual-objective architecture:
+1. **Vision Transformer (ViT)**: Acts as the state encoder, augmented with an Auxiliary Physics Distillation Head to predict privileged parameters (like mass or friction).
+2. **1D U-Net Vector Field**: A deterministic, parallelized Optimal Transport Flow Matching (Rectified Flows) model providing $O(1)$ generative inference latency.
+
+### Usage
+The checkpoints are saved in the highly-optimized **JAX / Orbax** format.
+
+```python
+import orbax.checkpoint as ocp
+
+# Example: Loading seed 42
+checkpoint_path = "seed_42"
+mngr = ocp.CheckpointManager(os.path.abspath(checkpoint_path))
+
+# Load the parameters
+restored = mngr.restore(mngr.latest_step())
+print(restored.keys())
+```

seed_1/_CHECKPOINT_METADATA

@@ -0,0 +1 @@
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seed_1/default/_METADATA

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seed_2/default/_sharding

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seed_42/default/_sharding

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