Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling
Liliang Ren Yang Liu Yadong Lu yelong shen Chen Liang Weizhu Chen
Efficiently modeling sequences with infinite context length has long been a challenging problem. Previous approaches have either suffered from quadratic computational complexity or limited extrapolation ability in length generalization. In this
work, we present Samba, a simple hybrid architecture that layer-wise combines
Mamba, a selective State Space Model (SSM), with Sliding Window Attention
(SWA). Samba selectively compresses a given sequence into recurrent hidden
states while still maintaining the ability to precisely recall recent memories with the
attention mechanism. We scale Samba up to 3.8B parameters with 3.2T training
tokens and demonstrate that it significantly outperforms state-of-the-art models
across a variety of benchmarks. Pretrained on sequences of 4K length, Samba
shows improved perplexity in context lengths of up to 1M in zero-shot. When
finetuned on 4K-length sequences, Samba efficiently extrapolates to a 256K context length with perfect memory recall on the Passkey Retrieval task, and exhibits
superior retrieval extrapolation on the challenging Phonebook task compared to
full-attention models. As a linear-time sequence model, Samba achieves a 3.73×
higher throughput compared to Transformers with grouped-query attention for user
prompts of 128K length, and a 3.64× speedup when generating 64K tokens with
unlimited streaming.
Large Language Models;Length Extrapolation;Efficiency;Hybrid State Space Models
Simple hybrid state space models outperform SOTA Transformers and SSMs.