Instructions to use hf-tiny-model-private/tiny-random-FNetForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-FNetForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-FNetForMaskedLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-FNetForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-FNetForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from hf-tiny-model-private/tiny-random-FNetForMaskedLM: direct link, hf CLI and curl.
- Browser
- Download file 708 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-FNetForMaskedLM/resolve/dbca3882464d2a449d3d83ddbfde391461d3df3a/spiece.model
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-FNetForMaskedLM@dbca3882464d2a449d3d83ddbfde391461d3df3a/spiece.model
-
curl -L -o spiece.model https://huggingface.co/hf-tiny-model-private/tiny-random-FNetForMaskedLM/resolve/dbca3882464d2a449d3d83ddbfde391461d3df3a/spiece.model
708 kB
- Xet hash:
- 8b34db02fa74e9ac7a4f26cf4f081eb5b1eb2ff1ac09ace00eb84040d771524f
- Size of remote file:
- 708 kB
- SHA256:
- 0f848afb4cb35389f15819aad6b9b4bb65d8b6cec9613b6145bea94b4a045ab6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.