Instructions to use amburger66/robometer-4b-lora-robotsmith-task02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amburger66/robometer-4b-lora-robotsmith-task02 with Transformers:
# Load model directly from transformers import AutoProcessor, RBM processor = AutoProcessor.from_pretrained("amburger66/robometer-4b-lora-robotsmith-task02") model = RBM.from_pretrained("amburger66/robometer-4b-lora-robotsmith-task02") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: Qwen/Qwen3-VL-4B-Instruct | |
| tags: | |
| - reward_model | |
| - rbm | |
| - preference_comparisons | |
| library_name: transformers | |
| # amburger66/robometer-4b-lora-robotsmith-task02 | |
| ## Model Details | |
| - **Base Model**: Qwen/Qwen3-VL-4B-Instruct | |
| - **Model Type**: qwen3_vl | |
| ## Training Run | |
| - **Wandb Run**: [lora_task02](https://wandb.ai/r-pad/rbm-finetune-robotsmith/runs/k51jvvii) | |
| - **Wandb ID**: `k51jvvii` | |
| - **Project**: rbm-finetune-robotsmith | |
| - **Notes**: fine-tuning Robometer on RobotSmith | |
| ## Citation | |
| If you use this model, please cite: | |