TIDE augments standard transformers with per-layer token embedding injection via an ensemble of memory blocks and a depth-conditioned router to mitigate rare-token undertraining and contextual collapse.
Lord: Low rank decomposi- tion of monolingual code llms for one-shot compression
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Ditto quantizes Code LLMs with per-block K-Means codebooks and compiles GEMV into BLAS calls, claiming up to 10.5× faster, 6.4× leaner local inference with ~0.27% pass@1 loss.
The paper surveys techniques to speed up and reduce the resource needs of LLM inference, organized by data-level, model-level, and system-level changes, with comparative experiments on representative methods.
citing papers explorer
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TIDE: Every Layer Knows the Token Beneath the Context
TIDE augments standard transformers with per-layer token embedding injection via an ensemble of memory blocks and a depth-conditioned router to mitigate rare-token undertraining and contextual collapse.
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Compiling Code LLMs into Lightweight Executables
Ditto quantizes Code LLMs with per-block K-Means codebooks and compiles GEMV into BLAS calls, claiming up to 10.5× faster, 6.4× leaner local inference with ~0.27% pass@1 loss.
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A Survey on Efficient Inference for Large Language Models
The paper surveys techniques to speed up and reduce the resource needs of LLM inference, organized by data-level, model-level, and system-level changes, with comparative experiments on representative methods.