Instructions to use pravsels/molmoact2_insert_candle_quantile_norm_fix_25k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use pravsels/molmoact2_insert_candle_quantile_norm_fix_25k with LeRobot:
- Notebooks
- Google Colab
- Kaggle
| library_name: lerobot | |
| tags: | |
| - molmoact2 | |
| - robotics | |
| - lerobot | |
| - vla | |
| - bimanual | |
| base_model: allenai/MolmoAct2 | |
| # molmoact2_insert_candle_quantile_norm_fix_25k | |
| Fine-tuned [MolmoAct2](https://huggingface.co/allenai/MolmoAct2) (action-expert-only) for `insert_candle` on SO101 data. | |
| | | | | |
| |---|---| | |
| | **Policy** | MolmoAct2 (`policy.type=molmoact2`) | | |
| | **Init checkpoint** | [allenai/MolmoAct2](https://huggingface.co/allenai/MolmoAct2) | | |
| | **Dataset** | [villekuosmanen/armnetbench_insert_candle](https://huggingface.co/datasets/villekuosmanen/armnetbench_insert_candle) | | |
| | **Task** | `insert_candle` | | |
| | **Action dim** | 12 (bimanual) | | |
| | **Cameras** | `top`, `left_wrist`, `right_wrist` | | |
| | **Training** | 25k steps, QUANTILES norm, freeze, batch 32 global, Isambard GH200 | | |
| | **Prior HF repo** | [pravsels/molmoact2_insert_candle](https://huggingface.co/pravsels/molmoact2_insert_candle) | | |
| | **W&B project** | [molmoact2_insert_candle_quantile_norm_fix_25k](https://wandb.ai/pravsels/molmoact2_insert_candle_quantile_norm_fix_25k) | | |
| | **W&B run** | [b07rlk7i](https://wandb.ai/pravsels/molmoact2_insert_candle_quantile_norm_fix_25k/runs/b07rlk7i) | | |
| ## Checkpoints | |
| The checkpoint (local step `025000`, 25k training steps) lives at the **repository root** for direct loading. | |
| ## Verification | |
| | **Checkpoint step** | `025000` | | |
| | **Source path** | `checkpoints/025000/pretrained_model/` | | |
| | **model.safetensors** | 10,884,573,720 bytes, sha256 `af86903eebd5c089c4e74c694db6345671569d38e23da12840159744e8f4b593` | | |
| | **policy_preprocessor.json** | 2,523 bytes, sha256 `aac2ee4e61c26a5322c9b3e2f727ce060c90071e012185d9f39cceb2d43ea04a` | | |
| | **policy_postprocessor.json** | 758 bytes, sha256 `8690a8e7015281571c9de7d88073b302cd03123e5f677ea582c669dbf014e7ad` | | |
| | **train_config.json** | 8,836 bytes, sha256 `2ac88bc6f39c14eb153b6dd8a59e6686a51e1d2c2647206573d8dde433c82d1f` | | |
| Verify after download: | |
| ```bash | |
| sha256sum model.safetensors | |
| # expected: af86903eebd5c089c4e74c694db6345671569d38e23da12840159744e8f4b593 | |
| ``` | |
| ## Usage | |
| ```python | |
| from lerobot.policies.molmoact2.modeling_molmoact2 import MolmoAct2Policy | |
| policy = MolmoAct2Policy.from_pretrained("pravsels/molmoact2_insert_candle_quantile_norm_fix_25k") | |
| ``` | |