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Achieving near-FP16 accuracy with 4-bit quantization, TwinQuant offers a groundbreaking approach to optimizing large language model inference speed and efficiency.
LLMs under attack exhibit a tell-tale "Entropy Lull" – a period of abnormally low and stable token probability entropy – that can be exploited for highly accurate and efficient attack detection.
