Instructions to use poxonit/diffusion_chess_300_test_run_camdrop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use poxonit/diffusion_chess_300_test_run_camdrop with LeRobot:
- Notebooks
- Google Colab
- Kaggle
| { | |
| "checkpoint": "/home/sky/outputs/train/diffusion_chess_300_camdrop/checkpoints/last/pretrained_model", | |
| "dataset_repo": "AnonymousMouse404/chess_300", | |
| "n_eval_episodes": 20, | |
| "eval_episodes": [ | |
| 0, | |
| 20, | |
| 40, | |
| 60, | |
| 80, | |
| 100, | |
| 120, | |
| 140, | |
| 160, | |
| 180, | |
| 200, | |
| 220, | |
| 240, | |
| 260, | |
| 280, | |
| 300, | |
| 320, | |
| 340, | |
| 360, | |
| 380 | |
| ], | |
| "n_eval_batches": 214, | |
| "n_eval_frames": 6819, | |
| "diffusion_loss": 0.0050743053072318765, | |
| "action_mse": 14.814462722646935, | |
| "action_l1": 1.4622837310022183, | |
| "note": "Offline action-prediction metrics on dataset frames (not closed-loop task success). Evaluated within the trained episode prefix, so this reflects training-set fit rather than held-out generalization. action_mse / action_l1 are in the raw action space (joint positions, degrees)." | |
| } |