Popular GitHub Repositories for Ssms

Integrating Mamba/SSMs with Transformer for Enhanced Long Context and High-Quality Sequence Modeling

List of papers related to State Space Models (Mamba) in Vision.

[CVPR'25 Highlight] The official implementation of "GG-SSMs: Graph-Generating State Space Models"

Implementation of MambaFormer in Pytorch ++ Zeta from the paper: "Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks"

DAViMNet: SSMs-Based Domain Adaptive Object Detection

Implementation of a Hierarchical Mamba as described in the paper: "Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling"

A simple and minimal open source implementation of "Introducing LFM2: The Fastest On-Device Foundation Models on the Market" from Liquid AI in Pytorch

DAViMNet: SSMs-Based Domain Adaptive Object Detection

Transformers + Mambas + LSTMS All in One Model

A PyTorch implementation of the Hyena-Y model, a convolution-based multi-hybrid architecture optimized for edge devices.

The system enables sophisticated coordination of multiple drones through natural language commands, visual inputs, and real-time environmental data.

We introduce VLM-Mamba, the first Vision-Language Model built entirely on State Space Models (SSMs), specifically leveraging the Mamba architecture.

This repository presents a production-grade implementation of a transformer-based text embedding model inspired by OpenAI's text-embedding-ada-002.

An experimental repository on research for training multiple models all at once in an evolutionary capacity!

A carefully curated collection of high-quality libraries, projects, tutorials, research papers, and other essential resources focused on Structured State Space Models (SSMs), with a special emphasis on S4, Mamba, and their variants.

A research project implementing state-of-the-art sequence modeling architectures, focusing on State Space Models (SSMs) and their variants.

Data warehouse harga crypto dengan ETL (SSIS + SSMS) dan visualisasi Power BI. End-to-end data engineering.

Comparing length generalization performance for Mamba, attention-based transformers and Mamba-attention hybrids