File size: 2,249 Bytes
3d355f2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
# GR00T-N1.5-3B LoRA Fine-tuned Model
This is a LoRA fine-tuned checkpoint of [nvidia/GR00T-N1.5-3B](https://huggingface.co/nvidia/GR00T-N1.5-3B) trained on single front camera data.
## Model Details
- **Base Model**: nvidia/GR00T-N1.5-3B
- **Training Method**: LoRA (Low-Rank Adaptation)
- **Training Steps**: 100,000
- **Final Training Loss**: 0.053
## Training Configuration
### LoRA Parameters
- **Rank (r)**: 8
- **Alpha**: 16
- **Dropout**: 0.1
- **Target Modules**: to_q, to_k, to_v (attention layers only)
- **Trainable Parameters**: 1,638,400 (0.06% of total)
### Training Parameters
- **Batch Size**: 2 per GPU
- **Learning Rate**: 1e-4
- **Weight Decay**: 1e-5
- **Warmup Ratio**: 0.05
- **Optimizer**: AdamW
- **LR Scheduler**: Cosine
- **Training Duration**: ~1h 52m (6719 seconds)
- **Training Speed**: 14.88 steps/second
### Model Components Tuned
- **LLM Backbone**: ❌ Frozen
- **Vision Tower**: ❌ Frozen
- **Action Head Projector**: ✅ Tuned
- **Diffusion Model**: ✅ Tuned
## Dataset
- **Embodiment**: SO-100 robot with single front camera
- **Camera Resolution**: 320x240
- **FPS**: 30
- **Action Dimensions**: 6 (5 DoF arm + 1 gripper)
- **Action Horizon**: 16 timesteps
- **Video Backend**: torchvision_av
## Usage
This is a LoRA adapter that must be loaded on top of the base model:
```python
from gr00t.model.gr00t_n1 import GR00T_N1_5
from peft import PeftModel
# Load base model
base_model = GR00T_N1_5.from_pretrained("nvidia/GR00T-N1.5-3B")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "path/to/this/checkpoint")
# Use for inference
model.eval()
```
## Model Architecture
- **Action Dimension**: 32 (max)
- **Action Horizon**: 16
- **Hidden Size**: 2048
- **Compute Dtype**: bfloat16
- **Diffusion Timesteps**: 4 (inference)
## Training Hardware
- **GPUs**: 1x NVIDIA GPU
- **Compute Dtype**: bfloat16
- **TF32**: Enabled
- **Gradient Checkpointing**: Disabled
## Citation
If you use this model, please cite the original GR00T paper and model:
```bibtex
@misc{gr00t2024,
title={GR00T: Generalist Robot Policy},
author={NVIDIA},
year={2024},
url={https://huggingface.co/nvidia/GR00T-N1.5-3B}
}
```
## License
Inherits license from nvidia/GR00T-N1.5-3B base model.
|