Instructions to use pravsels/molmoact2_block_stack_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_block_stack_quantile_norm_fix_25k with LeRobot:
- Notebooks
- Google Colab
- Kaggle
| library_name: lerobot | |
| tags: | |
| - molmoact2 | |
| - robotics | |
| - lerobot | |
| - vla | |
| base_model: allenai/MolmoAct2 | |
| # molmoact2_block_stack_quantile_norm_fix_25k | |
| Fine-tuned [MolmoAct2](https://huggingface.co/allenai/MolmoAct2) (action-expert-only) for `block_stack` on SO101 data. | |
| | | | | |
| |---|---| | |
| | **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` | | |
| | **Action dim** | 6 (single-arm) | | |
| | **Cameras** | `top`, `wrist`, `front` | | |
| | **Training** | 25k steps, QUANTILES norm, freeze, batch 32 global, Isambard GH200 | | |
| | **Prior HF repo** | [pravsels/molmoact2_block_stack](https://huggingface.co/pravsels/molmoact2_block_stack) | | |
| | **W&B project** | [molmoact2_block_stack_quantile_norm_fix_25k](https://wandb.ai/pravsels/molmoact2_block_stack_quantile_norm_fix_25k) | | |
| | **W&B run** | [pir9oxln](https://wandb.ai/pravsels/molmoact2_block_stack_quantile_norm_fix_25k/runs/pir9oxln) | | |
| ## 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 `e4f71871567b158cecdc5762dc90f0fff98aa79087141754e7cb6d87bc928ea3` | | |
| | **policy_preprocessor.json** | 2,495 bytes, sha256 `e7c8b8293cb0265a01f83278033272efcb21b4c7bfb031cfbd683ed74ee7b139` | | |
| | **policy_postprocessor.json** | 757 bytes, sha256 `6dbed1e1ec69e8c50f3a04c1f144a54231e3ef508f15fd7896ead43ea645b033` | | |
| | **train_config.json** | 8,257 bytes, sha256 `3373d72a00976be8d2faf88869a84d67ba0852287d9305dc0f008419b65788c2` | | |
| Verify after download: | |
| ```bash | |
| sha256sum model.safetensors | |
| # expected: e4f71871567b158cecdc5762dc90f0fff98aa79087141754e7cb6d87bc928ea3 | |
| ``` | |
| ## Usage | |
| ```python | |
| from lerobot.policies.molmoact2.modeling_molmoact2 import MolmoAct2Policy | |
| policy = MolmoAct2Policy.from_pretrained("pravsels/molmoact2_block_stack_quantile_norm_fix_25k") | |
| ``` | |