Instructions to use vxbrandon/pruned_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vxbrandon/pruned_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="vxbrandon/pruned_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("vxbrandon/pruned_model") model = AutoModelForQuestionAnswering.from_pretrained("vxbrandon/pruned_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a19658c9896ba97dc73517feece1dbd92928fd495558b07f38f52ff6bd97b504
- Size of remote file:
- 379 MB
- SHA256:
- efcc69e1f1f61b1c73592e8c0c456fefbc1931de2398811a54710d3bb8fdccbe
路
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