Instructions to use MindNetML/dummy-model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MindNetML/dummy-model2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="MindNetML/dummy-model2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("MindNetML/dummy-model2") model = AutoModelForMaskedLM.from_pretrained("MindNetML/dummy-model2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3f2fd5b977659aedeb6e7fc5d8c349ecda00785e2822fec2aa24b30acfc391c
- Size of remote file:
- 433 MB
- SHA256:
- b4702c87f82387fdf93f625d9f7db02553a39747303733b2cc014c25862f80ef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.