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
- Xet hash:
- 6f225b4db5f8b1c2beae8800a297007b75f6eff9010ffe90706552fdb90ce997
- Size of remote file:
- 10.5 GB
- SHA256:
- 73ec9c3b09fbef9f7fdc9bb4e0d6e7979ce4c222facc54478a549f3a29960021
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