Instructions to use sshleifer/tiny-distilroberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/tiny-distilroberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sshleifer/tiny-distilroberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/tiny-distilroberta-base") model = AutoModelForMaskedLM.from_pretrained("sshleifer/tiny-distilroberta-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sshleifer/tiny-distilroberta-base: direct link, hf CLI and curl.
- Browser
- Download file 618 kB
-
https://huggingface.co/sshleifer/tiny-distilroberta-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sshleifer/tiny-distilroberta-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sshleifer/tiny-distilroberta-base/resolve/main/pytorch_model.bin
618 kB
- Xet hash:
- 24932a29a1407d8baf4e067b4f93332e7bb978ef2074f3c264d0d64ef7ea9d32
- Size of remote file:
- 618 kB
- SHA256:
- af63ccc6c4339b93271cea77aae4a5b69ccb88fd21b6d04d991dc5d0e5f4971e
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