VISION MODELS

A Deep Dive into Modern Vision Architectures: ViTs, Mamba Layers, STORM, SigLIP, and Qwen

Introduction As the AI landscape rapidly evolves, vision architectures are undergoing a revolution. We’ve moved beyond CNNs into the age of Vision Transformers (ViTs), hybrid systems like SigLIP, long-sequence models such as Mamba, and powerful multimodal models like Qwen-VL. Then there’s STORM—a new architecture combining selective attention, token reduction, and memory. This blog walks you…

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NEOVLM

Implementing KV Cache from Scratch in nanoVLM: A 38% Speedup in Autoregressive Generation

Introduction Autoregressive language models generate text one token at a time. Each new prediction requires a full forward pass through all transformer layers, leading to redundant computations. For example, generating the next token in: [What, is, in,] → [the] requires recomputing attention over [What, is, in,] even though these tokens haven’t changed. KV Caching solves this inefficiency by…

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