LKBQ: Pushing the Limit of Post-Training Quantization to Extreme 1 bit
Bibliographic record
Abstract
Recent advances have shown the potential for post-training quantization (PTQ) to reduce excessive hardware resources and quantize deep models to low bits in a short time, compared with Quantization-Aware Training (QAT). However, existing PTQ approaches lose a lot of accuracies when quantizing the model to extremely low bits, e.g., 1 bit. In this work, we propose layer-by-layer self-knowledge distillation binary post-training quantization (LKBQ), the first method capable of quantizing the weights of neural networks to 1 bit in PTQ domain. We show that careful use of layer-by-layer self-distillation within the LKBQ can provide a significant performance boost. Furthermore, our evaluation results show that the initialization of quantized network weights can have a huge impact on the results. Then we propose three methods for weight initialization. Finally, in light of the characteristics of the binarized network, we propose a method named gradient scaling to further improve efficiency. Our experiments show that LKBQ pushes the limit of PTQ to extreme 1-bit for the first time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".