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A Visual Guide to Mamba and State Space Models

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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.1 You can find its official implementation and model checkpoints in its repository. To see a Table of Contents (ToC), click on the stack of lines on the left-hand side. Thanks for reading Exploring Language Models! Subscribe to receive new posts on Gen AI and the book: Hands-On Large Language Models 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 h

A Visual Guide to Mamba and State Space Models An Alternative to Transformers for Language Modeling Maarten Grootendorst Feb 19, 2024 375 24 33 Share Translations - Korean UPDATE 🔥 - Now with animations ! 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 Sta

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