Instructions to use Shubhamai/tiny-random-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shubhamai/tiny-random-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shubhamai/tiny-random-distilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shubhamai/tiny-random-distilbert") model = AutoModelForSequenceClassification.from_pretrained("Shubhamai/tiny-random-distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Shubhamai/tiny-random-distilbert: direct link, hf CLI and curl.
- Browser
- Download file 353 kB
-
https://huggingface.co/Shubhamai/tiny-random-distilbert/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Shubhamai/tiny-random-distilbert/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Shubhamai/tiny-random-distilbert/resolve/main/flax_model.msgpack
353 kB
- Xet hash:
- 0f0754ab28d0c26b94562f233a87c5d58577aaf4cb1ad4219077317f2fe03b70
- Size of remote file:
- 353 kB
- SHA256:
- 9e3c03637235b82066860697f861a5460faf96973bdcce5e161e920764fb637b
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