Instructions to use mabidan/gemmafa-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mabidan/gemmafa-tokenizer with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mabidan/gemmafa-tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2bd2dd08192b2bf50e41af936ef389e287bc8c2c189acbc423d7af5177e492f0
- Size of remote file:
- 25.5 MB
- SHA256:
- 1b2e3bc0f30b6cb9fc3488d6a04f1f349a7e74c83076664f59734d2bda1e26f6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.