Inference
State Space Models (SSM)
State space models are sequence models that carry a fixed-size hidden state forward through the input, updating it with learned linear equations, rather than comparing every token to every other token like attention.
In depth
State space models are sequence models that carry a fixed-size hidden state forward through the input, updating it with learned linear equations, rather than comparing every token to every other token like attention. That gives linear scaling with sequence length and constant memory per generated token. The S4 paper made SSMs efficient enough to compete on long-range benchmarks, and later work like Mamba brought them to language modeling.
Primary source: arxiv.org
Example
State space models are sequence models that carry a fixed-size hidden state forward through the input, updating it with learned linear equations, rather than comparing every token to every other token like attention.
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FAQ
Common questions
What is State Space Models (SSM)?
State space models are sequence models that carry a fixed-size hidden state forward through the input, updating it with learned linear equations, rather than comparing every token to every other token like attention.
Why does State Space Models (SSM) matter for AI developers?
State Space Models (SSM) sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about State Space Models (SSM)?
Developers Digest publishes tutorials and videos that cover Inference topics including State Space Models (SSM). Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Mamba
Mamba is a sequence model architecture built on selective state space layers, where the SSM parameters depend on the current token so the model can choose what to remember or forget.
Inference
Streaming
Delivering model output token-by-token as it is generated rather than waiting for the full response.
Inference
Attention Mechanism
The core technique inside transformers that lets a model weigh the relevance of every token relative to every other token in a sequence.
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