MemoRizz: A Python library serving as a memory layer for AI applications. Leverages popular databases and storage solutions to optimize memory usage. Provides utility classes and methods for efficient data management, including MongoDB integration and OpenAI embeddings for semantic search capabilities.
Run larger LLMs with longer contexts on Apple Silicon by using differentiated precision for KV cache quantization. KVSplit enables 8-bit keys & 4-bit values, reducing memory by 59% with <1% quality loss. Includes benchmarking, visualization, and one-command setup. Optimized for M1/M2/M3 Macs with Metal support.
A fast and memory-optimized string library for heavy-text manipulation in Python
This code repository contains the code used for my "Optimizing Memory Usage for Training LLMs and Vision Transformers in PyTorch" blog post.
Automatically reduce the memory size of any pandas dataframe based on downcasting bit types efficiently
Training a lightweight GPT model (12.3M parameters) optimized for consumer GPUs with 8GB VRAM like the RTX 3050.
Reverse Overflow Multiple Stack: Space-efficient dynamic stack array structure
Smaller Arrays Implementations fully built in python 3.8
Hexel is an AI-native OS enabling autonomous decision-making and multi-agent collaboration across various deployments.
A powerful, memory-optimized desktop application for running Mistral 7B locally on resource-constrained systems
Optimiza tu sistema Control total de Win Gestión con seguridad Administra procesos Acelera tu Windows Potencia tu PC fácil Tu PC sin procesos Gaming sin límites Cierra lo innecesario Limpia y optimiza ya
Space-Efficient Neural Network Inference: O(√n) Memory Breakthrough - Making Large AI Models Accessible on Consumer Hardware
🚀 Comprehensive toolkit for analyzing and optimizing GitHub Copilot performance in large codebases. Includes memory monitoring, workspace analysis, and theoretical foundations with 60-80% memory reduction.
🚀 Comprehensive toolkit for analyzing and optimizing GitHub Copilot performance in large codebases. Includes memory monitoring, workspace analysis, and theoretical foundations with 60-80% memory reduction.
Enhanced Convolutional Neural Network Accelerators with Memory Optimization for Routing Applications
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