Instructions to use shant0602/groot-n1.5-lora-gr1-cansort with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shant0602/groot-n1.5-lora-gr1-cansort with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/root/.cache/huggingface/hub/models--nvidia--GR00T-N1.5-3B/snapshots/869830fc749c35f34771aa5209f923ac57e4564e") model = PeftModel.from_pretrained(base_model, "shant0602/groot-n1.5-lora-gr1-cansort") - Notebooks
- Google Colab
- Kaggle
GR00T N1.5 LoRA โ GR-1 Arms-Only CanSort
LoRA adapter fine-tuned from nvidia/GR00T-N1.5-3B on the GR-1 humanoid arms-only CanSort task using the NVIDIA GR00T-X-Embodiment-Sim dataset.
Training details
| Parameter | Value |
|---|---|
| Base model | nvidia/GR00T-N1.5-3B |
| Embodiment | gr1 |
| Data config | fourier_gr1_arms_only |
| Dataset | gr1_arms_only.CanSort (316,878 samples) |
| Steps | 20,000 |
| Batch size | 16 |
| Learning rate | 1e-4 (cosine decay) |
| LoRA rank | 64 |
| LoRA alpha | 128 |
| LoRA dropout | 0.1 |
| Tune diffusion model | false |
| GPU | A100 40GB |
| Final loss | 0.0174 |
| Training time | ~2 hours |
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Model tree for shant0602/groot-n1.5-lora-gr1-cansort
Base model
nvidia/GR00T-N1.5-3B