Instructions to use RobotisSW/Groot-n17-PaperTowelRoll-Tasks726-729-RightArmOnly-Filtered with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobotisSW/Groot-n17-PaperTowelRoll-Tasks726-729-RightArmOnly-Filtered with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import Gr00tN1d7 model = Gr00tN1d7.from_pretrained("RobotisSW/Groot-n17-PaperTowelRoll-Tasks726-729-RightArmOnly-Filtered", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Groot-n17-PaperTowelRoll-Tasks726-729-RightArmOnly-Filtered
Full resumable training checkpoint at optimizer step 4,000. The original cosine scheduler horizon is 20,000 steps.
Inputs: cam_left_head, cam_left_wrist, cam_right_wrist; arm_left and arm_right state. Output: arm_right only, 24-step action horizon. Effective global batch size: 96.
This checkpoint was produced by a recovery run from nvidia/GR00T-N1.7-3B with the same filtered Tasks 726โ729 dataset and original hyperparameters. The preceding run terminated at step 3,836 during an attempted live stop-control change, before its first scheduled checkpoint; those unsaved states were not recovered.
The model weights, both native DeepSpeed ZeRO-2 optimizer shards, scheduler, trainer state, and both rank RNG states are included. Native optimizer-state resume requires 2 GPU ranks. See resume/RESUME.md for download, environment setup, dataset transfer, and launch instructions.
The training dataset is not included in this public model repository.
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Base model
nvidia/GR00T-N1.7-3B