Model release

.gitattributes

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README.md

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+# oBERT-12-downstream-dense-QAT-squadv1
+
+This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
+
+It corresponds to the model presented in the `Table 3 - 12 Layers - 0% Sparsity - QAT`, and it represents an upper bound for performance of the corresponding pruned and quantized models:
+- 80% unstructured QAT: `neuralmagic/oBERT-12-downstream-pruned-unstructured-80-QAT-squadv1`
+- 80% block-4 QAT: `neuralmagic/oBERT-12-downstream-pruned-block4-80-QAT-squadv1`
+- 90% unstructured QAT: `neuralmagic/oBERT-12-downstream-pruned-unstructured-90-QAT-squadv1`
+- 90% block-4 QAT: `neuralmagic/oBERT-12-downstream-pruned-block4-90-QAT-squadv1`
+
+SQuADv1 dev-set:
+```
+EM = 81.99
+F1 = 89.06
+```
+
+Code: _coming soon_
+
+## BibTeX entry and citation info
+```bibtex
+@article{kurtic2022optimal,
+  title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
+  author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
+  journal={arXiv preprint arXiv:2203.07259},
+  year={2022}
+}
+```

all_results.json

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config.json

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eval_nbest_predictions.json

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pytorch_model.bin

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recipe.yaml

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+!QuantizationModifier
+    disable_quantization_observer_epoch: 5
+    end_epoch: -1.0
+    freeze_bn_stats_epoch: 5
+    quantize_embeddings: 1
+    start_epoch: 0.0
+    submodules: ['bert.encoder', 'bert.embeddings', 'qa_outputs']
+

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