Unverified50% confidenceFactExact time
Quantization typically brings a 1%–3% accuracy loss, but for discriminative tasks with a limited label space such as intent classification it is almost imperceptible.
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10/12/2026
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Related Claims
UnverifiedTernary quantization is applied only to the encoder and not the decoder, which is one of the key reasons for the small accuracy loss.77% similarVerified量化会不可避免地引入精度损失，在逻辑推理等对准确性敏感的任务上可能出现性能下降77% similarUnverified4-bit quantization introduces some accuracy loss, but in many practical tasks its performance is very close to that of the original model.74% similarUnverifiedCommonly used aggregate metrics (such as overall accuracy and evidence coverage) miss claim-level patterns, and macro-level numbers can become disconnected from actual per-claim changes.73% similarUnverifiedThe perplexity loss of Q4_K_M quantization compared to FP16 is typically within 1%–3%73% similar
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