Instructions to use alternis/gemma-desc-to-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alternis/gemma-desc-to-code with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("alternis/gemma-desc-to-code", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f061c7797e0feb9e49aff2d9fe5bdc7dc426737f0ff91448e22ab108d20e0663
- Size of remote file:
- 6.23 kB
- SHA256:
- 386ee6983873855c950a653f04fa6935de229bd9f1bce803767dee4c0e1a59ed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.