Instructions to use mcurmei/unique_N_max with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcurmei/unique_N_max with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mcurmei/unique_N_max")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mcurmei/unique_N_max") model = AutoModelForQuestionAnswering.from_pretrained("mcurmei/unique_N_max") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a20ca10f78f6fef0ff5b19edcb6b83447160189d5a31573b6374b80ab3905f52
- Size of remote file:
- 265 MB
- SHA256:
- 80d6bc23d5a29d4e6deaa915ecff2836321f1bec64754b5506e3d65b0a6ea368
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.