Instructions to use dandelin/vilt-b32-mlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dandelin/vilt-b32-mlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dandelin/vilt-b32-mlm")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMaskedLM processor = AutoProcessor.from_pretrained("dandelin/vilt-b32-mlm") model = AutoModelForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dandelin/vilt-b32-mlm: direct link, hf CLI and curl.
- Browser
- Download file 543 MB
-
https://huggingface.co/dandelin/vilt-b32-mlm/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dandelin/vilt-b32-mlm/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dandelin/vilt-b32-mlm/resolve/main/pytorch_model.bin
543 MB
- Xet hash:
- a4069c18d6199252f61420bc30a54cb43248ce6319db40f4c79a2e289a5f5fa1
- Size of remote file:
- 543 MB
- SHA256:
- e9437103e694deabdba02cbb9220caa184b82f21b956ca837a08e9d5015dff40
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