Скачать или смотреть Hybrid LLMs: Transformers + Mamba, Benchmarked

In this AI Research Roundup episode, Alex discusses the paper:
'Hybrid Architectures for Language Models: Systematic Analysis and Design Insights'
This study systematically evaluates hybrid language-model designs that fuse Transformer self-attention with structured state space models (Mamba/Mamba2) for long-context tasks. The authors compare inter-layer (alternating blocks with varying ratios) and intra-layer (parallel head-wise fusion) strategies across model scales from 100M to 3B parameters under matched compute/data budgets. They benchmark NLL, few-shot tasks, long-context retrieval, FLOPs, training time, and cache costs, providing design guidance on block ratios, layer ordering, and fusion choices. Extensive ablations illuminate scaling behavior and practical trade-offs in quality vs. efficiency for hybrid LLMs.
Paper URL: https://arxiv.org/abs/2510.04800

#AI #MachineLearning #DeepLearning #LLM #Transformers #Mamba #StateSpaceModels #LongContext