Papers by Jacob Nielsen
Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models? (2025.findings-acl)
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| Challenge: | Quantization-aware training of large language models reduces the precision of model parameters and reduces memory usage and energy consumption at inference time. |
| Approach: | They propose a method where models are first trained with 16-bit precision and then transition to 1.58-bit quantization-aware training. |
| Outcome: | The proposed training strategy reduces memory and energy consumption while maintaining model accuracy while reducing memory and inference time. |