Instructions to use jesbu1/robometer-4b-fft-armnet-tiled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jesbu1/robometer-4b-fft-armnet-tiled with Transformers:
# Load model directly from transformers import AutoProcessor, RBM processor = AutoProcessor.from_pretrained("jesbu1/robometer-4b-fft-armnet-tiled") model = RBM.from_pretrained("jesbu1/robometer-4b-fft-armnet-tiled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
jesbu1/robometer-4b-fft-armnet-tiled
Model Details
- Base Model: Qwen/Qwen3-VL-4B-Instruct
- Model Type: qwen3_vl
Tiled Armnet Specification
This checkpoint was full-fine-tuned on synchronized Armnet camera views tiled
into one 640 x 540 RGB frame.
- Single-arm layout: large
topview abovefrontandwristviews. - Bimanual layout: large
topview aboveleft_wristandright_wristviews. - Native episode labels preserved:
successful,failure, andsuboptimal. - Tiling utility:
dataset_upload/helpers.py:tile_synchronized_views. - Training datasets:
jesbu1/armnetbench_v01_tiled_so101jesbu1/armnetbench_v01_tiled_bimanual_so101
Training-time metrics at step 1000:
| Dataset | Pearson | Kendall |
|---|---|---|
| Single-arm SO-101 | 0.9224 | 0.9933 |
| Bimanual SO-101 | 0.9553 | 0.9333 |
Citation
If you use this model, please cite:
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Model tree for jesbu1/robometer-4b-fft-armnet-tiled
Base model
Qwen/Qwen3-VL-4B-Instruct