Instructions to use stevems1/distilroberta-base-SmithsModel2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stevems1/distilroberta-base-SmithsModel2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="stevems1/distilroberta-base-SmithsModel2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("stevems1/distilroberta-base-SmithsModel2") model = AutoModelForMaskedLM.from_pretrained("stevems1/distilroberta-base-SmithsModel2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from stevems1/distilroberta-base-SmithsModel2: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/stevems1/distilroberta-base-SmithsModel2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://stevems1/distilroberta-base-SmithsModel2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/stevems1/distilroberta-base-SmithsModel2/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- d8e87fe0a9363aa88a9f8aea505199590ac17ecaf420ffe2a5080047b6a18f6a
- Size of remote file:
- 329 MB
- SHA256:
- df2b44611a35ce8a8beb154c17f430c09f7bf6604d922a8d6b8b6eaff90cbd8e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.