Instructions to use pravsels/molmoact2_block_stack_base_quantile_12k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use pravsels/molmoact2_block_stack_base_quantile_12k with LeRobot:
- Notebooks
- Google Colab
- Kaggle
| library_name: lerobot | |
| tags: | |
| - molmoact2 | |
| - robotics | |
| - lerobot | |
| - vla | |
| - quantile-normalization | |
| base_model: allenai/MolmoAct2 | |
| # molmoact2_block_stack_base_quantile_12k | |
| Fine-tuned [MolmoAct2](https://huggingface.co/allenai/MolmoAct2) (action-expert-only) on block_stack with **QUANTILES** normalization (q01/q99). Intermediate checkpoint at step 12000 while 30k training continues on Isambard. | |
| | | | | |
| |---|---| | |
| | **Policy** | MolmoAct2 (`policy.type=molmoact2`) | | |
| | **Init checkpoint** | [allenai/MolmoAct2](https://huggingface.co/allenai/MolmoAct2) | | |
| | **Dataset** | [villekuosmanen/armnetbench_block_stack](https://huggingface.co/datasets/villekuosmanen/armnetbench_block_stack) | | |
| | **Task** | `block_stack` | | |
| | **Local run** | `molmoact2_block_stack_base_quantile` | | |
| | **Checkpoint step** | `012000` (12k / 30k target) | | |
| | **Normalization** | QUANTILES (action + state + gripper), IDENTITY (visual) | | |
| | **Training** | Isambard GH200, batch 64 (16/GPU x 4 DDP), bf16, no gradient checkpointing | | |
| ## Checkpoints | |
| Step `012000` lives at the **repository root** for direct loading. | |
| ## Usage | |
| ```python | |
| from lerobot.policies.molmoact2.modeling_molmoact2 import MolmoAct2Policy | |
| policy = MolmoAct2Policy.from_pretrained("pravsels/molmoact2_block_stack_base_quantile_12k") | |
| ``` | |