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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa5824ef464081185ce8ea6862ba78605c8a09015933ffbad02f7e5068bb6ea2
- Size of remote file:
- 4.98 GB
- SHA256:
- 6a23becbbbb3225bae4ff9d1c182fcf206f9d86b6314ee2ff55e0213a82c6c96
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