Instructions to use zharry29/step_benchmark_gpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zharry29/step_benchmark_gpt with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("zharry29/step_benchmark_gpt") model = AutoModel.from_pretrained("zharry29/step_benchmark_gpt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff13a4eeff4becd580e8305d0872a2dc53e3f57659f0c9f22cf7e0e111e6b6bb
- Size of remote file:
- 1.38 kB
- SHA256:
- 5d9c4543f1482b7b9f7edaed1ad26e506d81bd585c1041ff0aa5da65f149a720
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.