Papers by Markus Nagel
Quadapter: Adapter for GPT-2 Quantization (2022.findings-emnlp)
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| Challenge: | Quantization-aware training (QAT) fine-tunes model parameters in response to the numerical error arising from quantization. |
| Approach: | They propose a quantization adapter that scales activations channel-wise to make activations quantization-friendly by scaling them channel- wise. |
| Outcome: | The proposed method prevents overfitting and improves quantization performance. |
Understanding and Overcoming the Challenges of Efficient Transformer Quantization (2021.emnlp-main)
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| Challenge: | Recent advances in transformer quantization have shown remarkable improvement in many Natural Language Processing tasks and beyond. |
| Approach: | They propose a novel quantization scheme for transformers that can be quantized to ultra-low bit-widths, leading to significant memory savings with a minimum accuracy loss. |
| Outcome: | The proposed methods achieve state-of-the-art results on the GLUE benchmark using BERT, while preserving memory and accuracy. |