Instructions to use vxbrandon/pruned_model_iterative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vxbrandon/pruned_model_iterative with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="vxbrandon/pruned_model_iterative")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("vxbrandon/pruned_model_iterative") model = AutoModelForQuestionAnswering.from_pretrained("vxbrandon/pruned_model_iterative") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1629bb6325708bb4c19835a5b059b29afa5e6e59584637b5059ca440d200f5c
- Size of remote file:
- 265 MB
- SHA256:
- 9ccc19a7e404af3e8abced931c09a450a6d9d4bc537221e7c5d23f92579bd534
路
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