Papers › Q8BERT: Quantized 8Bit BERT
Q8BERT: Quantized 8Bit BERT
Ofir Zafrir, Guy Boudoukh, Peter Izsak, Moshe Wasserblat
Recently, pre-trained Transformer based language models such as BERT and GPT, have shown great improvement in many Natural Language Processing (NLP) tasks. However, these models contain a large amount of parameters. The emergence of even larger and more accurate models such as GPT2 and Megatron, suggest a trend of large pre-trained Transformer models. However, using these large models in production environments is a complex task requiring a large amount of compute, memory and power resources. In this work we show how to perform quantization-aware training during the fine-tuning phase of BERT in order to compress BERT by 4× with minimal accuracy loss. Furthermore, the produced quantized model can accelerate inference speed if it is optimized for 8bit Integer supporting hardware.
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Code
Syntology Ran 3 of 11 code samples harvested from 3 repositories linked to this paper; 8 have no recorded run. Of those that ran: 2 ran · honoured contract; 1 ran · our draft was wrong.
By repository: official repository: 5 samples from 1 repository, 0 ran; community (archive-listed): 5 samples from 2 repositories, 2 ran; 1 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
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Code Syntology ran Syntology
11 samples harvested; 3 ran; 2 honoured the contract we drafted; 8 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 3 of the 11 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 3 repositories linked to this paper, official or community; each sample names its own and says which. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Linguistic Acceptability | CoLA | Q8BERT (Zafrir et al., 2019) | Accuracy | 65.0 | #24 of 43 | Archive leaderboard | report |
| Natural Language Inference | MultiNLI | Q8BERT (Zafrir et al., 2019) | Matched | 85.6 | #29 of 67 | Archive leaderboard | report |
| Natural Language Inference | QNLI | Q8BERT (Zafrir et al., 2019) | Accuracy | 93.0 | #23 of 43 | Archive leaderboard | report |
| Natural Language Inference | RTE | Q8BERT (Zafrir et al., 2019) | Accuracy | 84.8 | #27 of 90 | Archive leaderboard | report |
| Semantic Textual Similarity | MRPC | Q8BERT (Zafrir et al., 2019) | Accuracy | 89.7 | #17 of 45 | Archive leaderboard | report |
| Semantic Textual Similarity | STS Benchmark | Q8BERT (Zafrir et al., 2019) | Pearson Correlation | 0.911 | #14 of 66 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | Q8BERT (Zafrir et al., 2019) | Accuracy | 94.7 | #32 of 87 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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