---
library_name: lerobot
tags:
- molmoact2
- robotics
- lerobot
- vla
- quantile-normalization
base_model: allenai/MolmoAct2-SO100_101
---

# molmoact2_block_stack_so101_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-SO100_101](https://huggingface.co/allenai/MolmoAct2-SO100_101) |
| **Dataset** | [villekuosmanen/armnetbench_block_stack](https://huggingface.co/datasets/villekuosmanen/armnetbench_block_stack) |
| **Task** | `block_stack` |
| **Local run** | `molmoact2_block_stack_so101_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_so101_quantile_12k")
```