MLA with rotary embeddings at half latent rank keeps validation loss nearly unchanged while cutting KV-cache memory by roughly half on small language models.
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Latent Multi-Head Attention for Small Language Models
MLA with rotary embeddings at half latent rank keeps validation loss nearly unchanged while cutting KV-cache memory by roughly half on small language models.