tbhou/sigma

This repo collects some latest research work of Generative AI. It provides simple implementations to understand the ideas and some follow-up discussions to inspire future work.

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This collection of research implementations helps machine learning researchers explore and understand the latest advancements in generative AI, particularly in areas like efficient attention mechanisms, Mixture-of-Experts (MoE) architectures, and auto-encoders for image and video generation. It provides simplified code examples and discussions to grasp the core ideas and inspire new directions. Researchers developing or studying generative AI models will find this useful.

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Use this if you are a machine learning researcher who wants to quickly understand and experiment with cutting-edge generative AI techniques like sparse attention or novel MoE designs, without building them from scratch.

Not ideal if you are an end-user looking for a pre-built application or a production-ready library for existing generative AI tasks.

Generative AI Research Machine Learning Development Deep Learning Architectures AI Model Optimization Vision-Language Models
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Python

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Last pushed

Apr 24, 2025

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