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# Model Details: QuaLA-MiniLM
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The article discusses the challenge of making transformer-based models efficient enough for practical use, given their size and computational requirements. The authors propose a new approach called **QuaLA-MiniLM**, which combines knowledge distillation, the length-adaptive transformer (LAT) technique, and low-bit quantization. We expand the Dynamic-TinyBERT approach. This approach trains a single model that can adapt to any inference scenario with a given computational budget, achieving a superior accuracy-efficiency trade-off on the SQuAD1.1 dataset. The authors compare their approach to other efficient methods and find that it achieves up to an **x8.8 speedup with less than 1% accuracy loss**. They also provide their code publicly on GitHub. The article also discusses other related work in the field, including dynamic transformers and other knowledge distillation approaches.
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### QuaLA-MiniLM training process
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Figure showing QuaLA-MiniLM training process. To run the model with the best accuracy-efficiency tradeoff per a specific computational budget, we set the length configuration to the best setting found by an evolutionary search to match our computational constraint.
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### Model license
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Licensed under MIT license.
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```
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For more code examples, refer to the GitHub Repo.
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### Metrics (Model Performance):
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| comments: | In this version we added reference to the source code in the abstract. arXiv admin note: text overlap with arXiv:2111.09645 |
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| Subjects: | Computation and Language (cs.CL) |
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| Cite as: | arXiv:2210.17114 [cs.CL]|
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| - | (or arXiv:2210.17114v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2210.17114|
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---
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license: mit
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---
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# Model Details: QuaLA-MiniLM
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The article discusses the challenge of making transformer-based models efficient enough for practical use, given their size and computational requirements. The authors propose a new approach called **QuaLA-MiniLM**, which combines knowledge distillation, the length-adaptive transformer (LAT) technique, and low-bit quantization. We expand the Dynamic-TinyBERT approach. This approach trains a single model that can adapt to any inference scenario with a given computational budget, achieving a superior accuracy-efficiency trade-off on the SQuAD1.1 dataset. The authors compare their approach to other efficient methods and find that it achieves up to an **x8.8 speedup with less than 1% accuracy loss**. They also provide their code publicly on GitHub. The article also discusses other related work in the field, including dynamic transformers and other knowledge distillation approaches.
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### QuaLA-MiniLM training process
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Figure showing QuaLA-MiniLM training process. To run the model with the best accuracy-efficiency tradeoff per a specific computational budget, we set the length configuration to the best setting found by an evolutionary search to match our computational constraint.
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[ArchitecureQuaLA-MiniLM.jpg](ArchitecureQuaLA-MiniLM.jpg)
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### Model license
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Licensed under MIT license.
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```
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### Metrics (Model Performance):
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| comments: | In this version we added reference to the source code in the abstract. arXiv admin note: text overlap with arXiv:2111.09645 |
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| Subjects: | Computation and Language (cs.CL) |
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| Cite as: | arXiv:2210.17114 [cs.CL]|
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| - | (or arXiv:2210.17114v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2210.17114|
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