Robotics
Core ML
LeRobot
coreai
core-ai
coreai-fabric
aimodel
apple
apple-silicon
on-device
reward-model
Instructions to use kevinqz/Robometer-4B-CoreAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use kevinqz/Robometer-4B-CoreAI with LeRobot:
- Notebooks
- Google Colab
- Kaggle
| { | |
| "recipe_id": "robometer-4b", | |
| "generated_at": "2026-07-07T18:42:12+00:00", | |
| "bundle": "build/robometer-4b/robometer-4b.aimodel", | |
| "conversion_manifest": null, | |
| "gate_a": { | |
| "status": "passed", | |
| "checks": [ | |
| { | |
| "name": "bundle_exists", | |
| "status": "passed", | |
| "detail": "build/robometer-4b/robometer-4b.aimodel" | |
| }, | |
| { | |
| "name": "bundle_files_present", | |
| "status": "passed", | |
| "detail": "3 expected file(s) present" | |
| }, | |
| { | |
| "name": "metadata_json_parses", | |
| "status": "passed", | |
| "detail": "metadata.json parses (3 top-level keys)" | |
| }, | |
| { | |
| "name": "metadata_matches_recipe", | |
| "status": "passed", | |
| "detail": "1 key(s) match" | |
| } | |
| ] | |
| }, | |
| "gate_b": { | |
| "metric": "graph_output_cosine", | |
| "threshold": 0.999, | |
| "tolerance": 0.0005, | |
| "value": 0.999999999996286, | |
| "status": "passed", | |
| "measurement_source": "graph-output-parity-measured.json", | |
| "min_cosine": 0.999999999996286, | |
| "median_cosine": 0.9999999999982424, | |
| "mean_cosine": 0.9999999999979249, | |
| "cosine_ci95": [ | |
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| ], | |
| "min_progress_cosine": 0.999999999998956, | |
| "min_success_cosine": 0.999999999996286, | |
| "per_obs_cosine": [ | |
| 0.9999999999981611, | |
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| "n_obs": 8, | |
| "hidden_dim": 2560, | |
| "progress_bins": 10, | |
| "frames": 8, | |
| "reference_dtype": "float32", | |
| "runner": "coreai-fabric-parity-runner/0.1.0", | |
| "environment": { | |
| "platform": "darwin-arm64", | |
| "accelerator": "apple_silicon", | |
| "runtime_version": "1.0.0b2", | |
| "coreai_torch": "0.4.1", | |
| "torch": "2.9.0", | |
| "transformers": "4.57.6" | |
| }, | |
| "reference": "Torch Robometer reward heads (progress + success) vs the Core AI .aimodel over identical seeded per-frame prog-token hidden states; non-autoregressive single forward (the Qwen3-VL-4B backbone is host-owned, not in the asset). The metric is the worst per-obs cosine across both output heads." | |
| }, | |
| "overall": "passed" | |
| } | |