Finedeep addresses the sparse activation problem in dense LLMs by introducing a novel architecture that partitions feed-forward neural networks into multiple specialized experts across sub-layers.

• Combats the inefficiency where many activation values approach zero
• Enables more efficient exploration of the model's representation space
• Creates fine-grained expert structures that better utilize model capacity
• Demonstrates improved performance over traditional dense architectures

This engineering innovation represents a significant advancement for more efficient and effective LLM architectures, potentially reducing computational waste while enhancing model capabilities.

Finedeep: Mitigating Sparse Activation in Dense LLMs via Multi-Layer Fine-Grained Experts