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:
- b2fc65a0a49c5994b5ad45fe8bdec1c756b41abc34f3f5887f897c618914e368
- Size of remote file:
- 3.96 kB
- SHA256:
- a1f9c4b6fc115929525d09486f6ce65c5b4d05a232d5f514db59b05c3f92f602
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.