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
File size: 561 Bytes
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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:
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