Instructions to use nmcco/17_gemma_eager with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nmcco/17_gemma_eager with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nmcco/17_gemma_eager", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dcdb49858261796182680ccba88c670452a853cb1098054b1272e65b01d7b15f
- Size of remote file:
- 6.84 kB
- SHA256:
- 4df429eac1a8511daa7500bef18bba4783e74de223e42bf0fa50ee19bd5c933e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.