Instructions to use quantumLeopard/calculator_model_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quantumLeopard/calculator_model_test with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("quantumLeopard/calculator_model_test") model = AutoModelForSeq2SeqLM.from_pretrained("quantumLeopard/calculator_model_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89fc796b994e3470ff3ef7219c5808290136756dad7cf5a76d0e29b960238227
- Size of remote file:
- 31.2 MB
- SHA256:
- 7d3010dfeb4a3be09fbb08f4001231c3d61258e71807430cba39ad57faa7a900
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.