Instructions to use RedHatAI/oBERT-12-downstream-pruned-unstructured-80-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/oBERT-12-downstream-pruned-unstructured-80-mnli with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RedHatAI/oBERT-12-downstream-pruned-unstructured-80-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add link to code
Browse files
README.md
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@@ -36,9 +36,11 @@ The dev-set performance reported in the paper is averaged over three seeds, and
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Code:
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```bibtex
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@article{kurtic2022optimal,
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title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
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| stdev | 0.140 | 0.133 |
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```
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Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT)
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If you find the model useful, please consider citing our work.
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## Citation info
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```bibtex
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@article{kurtic2022optimal,
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title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
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