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
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:24da96d892b6eda2a31165f2fe5a149c2e2b7e72687a5df5ef8e27ecd28ebd3c
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size 265470032
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