Text Classification
Transformers
PyTorch
Safetensors
English
hydra-bitnet
bitnet
Mixture of Experts
mixture-of-experts
1-bit
quantized
compression
security
m2m-protocol
Instructions to use infernet/hydra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use infernet/hydra with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="infernet/hydra")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("infernet/hydra", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5cf56e12c0c1df77be917aa45d81171b094890d78dda9747772b0cba5c9f6827
- Size of remote file:
- 38.9 MB
- SHA256:
- ad48d0d8972f925560c81f3685692cd661e501699f41c84a33aa7885f19d3b13
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