Instructions to use hamishivi/fixed-distilroberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamishivi/fixed-distilroberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hamishivi/fixed-distilroberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hamishivi/fixed-distilroberta-base") model = AutoModel.from_pretrained("hamishivi/fixed-distilroberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from hamishivi/fixed-distilroberta-base: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/hamishivi/fixed-distilroberta-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://hamishivi/fixed-distilroberta-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/hamishivi/fixed-distilroberta-base/resolve/main/flax_model.msgpack
329 MB
- Xet hash:
- 42c7d15ae09e3bd09c518681ffa670fa31de14f692fc20d370743cd794084a21
- Size of remote file:
- 329 MB
- SHA256:
- 206d967cebfe43a31c268bc2f349cb3206a34939eebabd3c05fb6caf040c8673
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.