Instructions to use MK-hugging/calculator_model_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MK-hugging/calculator_model_test with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MK-hugging/calculator_model_test") model = AutoModelForSeq2SeqLM.from_pretrained("MK-hugging/calculator_model_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 577e43b2291528594da5e1caf4351d7cef5805c0607c86d79c1dcb1776ce3c5c
- Size of remote file:
- 5.33 kB
- SHA256:
- a62cf924638f97c75cb2084f52c4986eed344c84beb51d6e367937a682015bdd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.