Instructions to use SparshSyde/moe_guard_gate_iter2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparshSyde/moe_guard_gate_iter2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SparshSyde/moe_guard_gate_iter2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SparshSyde/moe_guard_gate_iter2") model = AutoModelForSequenceClassification.from_pretrained("SparshSyde/moe_guard_gate_iter2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5a6f7709e2871c7afb2e180732507b5dc51301a5b2ecf398319b70e1cf3a361
- Size of remote file:
- 1.12 GB
- SHA256:
- 0731cd202ba773b31325d01d7ceeae53baaeaa23bb55f521818fcf2e0ed911bd
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