The authors introduce MLAfication, a four-component pipeline to convert standard attention (MHA/GQA) layers into MLA layers.
Method converts LLMs to efficient attention while handling QKNorm and MoE
MLAfication uses calibrated KNorm, parameter-efficient distillation, and other techniques to achieve up to 5.35x throughput and 94.73% KV-cache reduction.
Independent
Zile Wang · Zhide Xie · Tao Ji
Research Digest··3 min read
The authors present MLAfication, an extension of the MHA2MLA framework that adapts pretrained LLMs using multi-head or grouped-query attention to the more efficient Multi-Head Latent Attention (MLA) architecture.
Why this paper
Independent
In one line
MLAfication converts existing attention architectures to MLA, enabling up to 5.35x throughput and 94.73% KV-cache reduction.
What we could check
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