bit-allocation

本文介绍了Colla-Q，一种基于激活熵的比特分配框架，用于量化混合专家模型，以平衡专家性能，提高整体模型效率并减少对校准数据集的依赖。

KLQ is a training-free LLM quantization method that allocates bits per direction based on measured KL divergence, outperforming existing training-free rotation-based methods on W4A4KV4-bit settings for models like Llama 3.2 1B and Qwen 2.5.

提出Block-GTQ，一种感知RoPE的KV缓存量化比特分配方法，通过为高能量RoPE块分配更多比特，提升长上下文性能与内存效率。