Instructions to use lancer59/gemma2bit_adapter_aimath_tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lancer59/gemma2bit_adapter_aimath_tokenizer with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lancer59/gemma2bit_adapter_aimath_tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2ae82c2fdec172927dac4f5db4eb0a0563a89612597662308974d097cab614d
- Size of remote file:
- 17.5 MB
- SHA256:
- 4dee55b4e1c350b7fcbb944b8df041405d1e2b3cf19752c07885c29bad626a40
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.