Model release

.gitattributes

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README.md

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+# oBERT-3-downstream-pruned-block4-90-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 - 3 Layers - Sparsity 90% - 4-block + QAT`.
+
+```
+Pruning method: oBERT downstream block-4 + QAT
+Paper: https://arxiv.org/abs/2203.07259
+Dataset: SQuADv1
+Sparsity: 90%
+Number of layers: 3
+```
+
+The dev-set performance of this model:
+```
+EM = 70.00
+F1 = 79.66
+```
+
+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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pytorch_model.bin

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recipe.yaml

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+!LayerPruningModifier
+    end_epoch: -1.0
+    layers: ['bert.encoder.layer.3', 'bert.encoder.layer.4', 'bert.encoder.layer.5', 'bert.encoder.layer.6', 'bert.encoder.layer.7', 'bert.encoder.layer.8', 'bert.encoder.layer.9', 'bert.encoder.layer.10', 'bert.encoder.layer.11']
+    start_epoch: -1.0
+    update_frequency: -1.0
+
+!QuantizationModifier
+    disable_quantization_observer_epoch: 5.0
+    end_epoch: -1.0
+    freeze_bn_stats_epoch: 5.0
+    quantize_embeddings: 1
+    start_epoch: 0.0
+    submodules: ['bert.encoder', 'qa_outputs', 'bert.embeddings']
+

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