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A Visual Guide to Mamba and State Space Models - Maarten Grootendorst

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The Transformer architecture has been a major component in the success of Large Language Models (LLMs). It has been used for nearly all LLMs that are being used today, from open-source models like Mistral to closed-source models like ChatGPT. To further improve LLMs, new architectures are developed that might even outperform the Transformer architecture. One of these methods is Mamba, a State Space Model. Mamba was proposed in the paper Mamba: Linear-Time Sequence Modeling with Selective State Spaces. You can find its official implementation and model checkpoints in its repository. In this post, I will introduce the field of State Space Models in the context of language modeling and explore concepts one by one to develop an intuition about the field. Then, we will cover how Mamba might challenge the Transformers architecture. As a visual guide, expect many visualizations to develop an intuition about Mamba and State Space Models! To illustrate why Mamba is such an interesting architect

A Visual Guide to Mamba and State Space Models The Transformer architecture has been a major component in the success of Large Language Models (LLMs). It has been used for nearly all LLMs that are being used today, from open-source models like Mistral to closed-source models like ChatGPT. To further improve LLMs, new architectures are developed that might even outperform the Transformer architecture. One of these methods is Mamba , a State Space Model . Mamba was proposed in the paper Mamba: Linear-Time Sequence Modeling with Selective State Spaces . You can find its official implementation an

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