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 flax_model.msgpack from sshleifer/tiny-distilroberta-base: direct link, hf CLI and curl.
- Browser
- Download file 609 kB
-
https://huggingface.co/sshleifer/tiny-distilroberta-base/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/tiny-distilroberta-base@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/tiny-distilroberta-base/resolve/refs%2Fpr%2F1/flax_model.msgpack
609 kB
- Xet hash:
- 498bfe3d41f5f6106001ac169d24fd79ae59923a6ff7dfef856f0ed65fccfddb
- Size of remote file:
- 609 kB
- SHA256:
- 44ae739ff9dd7f45b29f3f4e6909658aeae152499be687af1eae9266a29e9e22
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