Instructions to use mgh6/temp2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgh6/temp2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mgh6/temp2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mgh6/temp2") model = AutoModelForMaskedLM.from_pretrained("mgh6/temp2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4ae409e03b2aa30659e75fa94f4f2f53e2bcfbba299ba4d152646dd91dfd65b
- Size of remote file:
- 3.96 kB
- SHA256:
- a96d88d506584433fef4419e0e4982c6bdd2769b36ab1dc28e8e9ef66dcc0ec6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.