Instructions to use sparsh35/sparshquantgemms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sparsh35/sparshquantgemms with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sparsh35/sparshquantgemms")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sparsh35/sparshquantgemms") model = AutoModelForSequenceClassification.from_pretrained("sparsh35/sparshquantgemms") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b710a8e3989751dc2c105f3dee3f93833cca2eb81982c411a24d393b0159a7c9
- Size of remote file:
- 17.5 MB
- SHA256:
- 7da53ca29fb16f6b2489482fc0bc6a394162cdab14d12764a1755ebc583fea79
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