| --- |
| license: mit |
| task_categories: |
| - robotics |
| tags: |
| - VLA |
| - vision-language-action |
| - pruning |
| - calibration |
| --- |
| |
| # VLADrop Calibration Data |
|
|
| Calibration sets for the layer-importance profiling in |
| **Drop-Then-Recovery (DTR): How Redundant Are Vision-Language-Action Models?** |
| ([paper](https://arxiv.org/abs/2606.27755) · [code](https://github.com/s1ghhh/VLADrop) · |
| [checkpoints](https://huggingface.co/collections/s1ghhh/vladrop-drop-then-recovery-dtr-checkpoints-6a509dd598cf54ae53060204)). |
|
|
| Each file holds the exact 512 samples (64 batches × 8) a profiling run consumes — not the full |
| dataset. Loading these reproduces the paper's GateProbe / baseline-metric block rankings exactly. |
|
|
| | File | Setting | Seed | |
| |---|---|---| |
| | `pi05_libero_dropped_calib_64x8_seed42.pt` | pi0.5 × LIBERO | 42 | |
| | `pi05_libero_plus_calib_64x8_seed42.pt` | pi0.5 × LIBERO-Plus | 42 | |
| | `openvla_libero_calib_64x8_seed9999.pt` | OpenVLA-OFT × LIBERO | 9999 | |
| | `openvla_libero_plus_calib_64x8_seed9999.pt` | OpenVLA-OFT × LIBERO-Plus | 9999 | |
|
|
| pi0.5 calibration is stored in bfloat16 (pi0.5's runtime precision) and is regenerated |
| deterministically by `profiling/pi0.5/dump_calib.py` in the code repo. |
|
|
| ## Usage |
|
|
| ```bash |
| huggingface-cli download s1ghhh/VLADrop_Calibration_Data --repo-type dataset \ |
| --local-dir profiling/calibration_data |
| ``` |
|
|
| See `profiling/README.md` in the [code repo](https://github.com/s1ghhh/VLADrop) for the full |
| reproduction commands. |
|
|
