L2A trains one LLM with input-and-budget-conditioned gates to adapt sparsity across layers, heads, and tokens, tracing the compute-accuracy frontier while staying within 0.6% of dense performance at 34% layer sparsity on tested models.
Qwen2.5 technical report.CoRR, 2024a
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End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference
L2A trains one LLM with input-and-budget-conditioned gates to adapt sparsity across layers, heads, and tokens, tracing the compute-accuracy frontier while staying within 0.6% of dense performance at 34% layer sparsity on tested models.