Edge 433: Samba, Unlimited Context Windows and State Space Models

How long of a context can SSM models process?

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In this issue:

1. An introduction to SAMBA and the idea of SSMs for long context windows.

2. A review of the original SAMBA paper.

3. Microsoft’s TaskWeaver agent for analytics workflows.

💡 ML Concept of the Day: SAMBA is an SSM for Long Context Windows

Modeling sequences with infinite context length is a challenging problem in AI. Many previous methods face difficulties due to either high computational costs or limited ability to handle sequences longer than those used in training. Samba offers a new solution with its hybrid architecture, blending Mamba, a selective State Space Model (SSM), with Sliding Window Attention (SWA) to tackle these issues.

Samba combines the strengths of Mamba and SWA to efficiently model long sequences. This architecture compresses sequences into hidden states for recurrent processing, while maintaining the ability to recall specific memories through the attention mechanism. By integrating these techniques, Samba achieves efficient computation with linear-time complexity, making it capable of generalizing to longer sequences while ensuring precise memory recall.