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
molmoact2_insert_candle_quantile_norm_fix_25k
Fine-tuned MolmoAct2 (action-expert-only) for insert_candle on SO101 data.
| Policy | MolmoAct2 (policy.type=molmoact2) |
| Init checkpoint | allenai/MolmoAct2 |
| Dataset | 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 |
| W&B project | molmoact2_insert_candle_quantile_norm_fix_25k |
| W&B run | 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:
sha256sum model.safetensors
# expected: af86903eebd5c089c4e74c694db6345671569d38e23da12840159744e8f4b593
Usage
from lerobot.policies.molmoact2.modeling_molmoact2 import MolmoAct2Policy
policy = MolmoAct2Policy.from_pretrained("pravsels/molmoact2_insert_candle_quantile_norm_fix_25k")
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Model tree for pravsels/molmoact2_insert_candle_quantile_norm_fix_25k
Base model
allenai/MolmoAct2