Awesome GitHub RepositoriesMathematical Problem Solving Toolkits

This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

This project is a comprehensive, crowdsourced directory of mathematical resources, functioning as a decentralized index of external educational materials. It organizes a vast collection of textbooks, lecture notes, and research tools into a hierarchical taxonomy, allowing users to navigate diverse mathematical disciplines through a version-controlled repository. The collection distinguishes itself by acting as a central hub for both academic discovery and practical application. It provides access to a wide array of interactive software, visualization tools, and step-by-step solvers designed t

This project is a cryptographic mining protocol that establishes a distributed compute network for solving complex mathematical tasks. It functions as a decentralized infrastructure where registered mining nodes participate in proof of work mining to solve network problems in exchange for rewards. The system specializes in quantum-inspired problem solving by mapping tasks into Ising mathematical structures. These problems are processed using a hardware-agnostic computation model, allowing solvers to execute tasks across CPU, GPU, or quantum processing units. The protocol includes tools for m

This project is a comprehensive reference for algorithms and data structures used to solve complex computational problems in competitive programming. It serves as a technical resource for implementing advanced mathematical programming, computational geometry, and graph theory. The repository provides detailed implementation guides for diversifying algorithmic techniques, including top-down and bottom-up dynamic programming optimization, number theory, and linear algebra. It features specific guides for complex tasks such as constructing planar graphs, solving linear Diophantine equations, and

OpenCompass is an open-source framework for standardized benchmarking of large language models. It provides a configurable evaluation pipeline that supports both objective and subjective assessment, using a dual-engine architecture to handle closed-form answer comparison and open-ended response rating. The framework is designed as a modular platform where datasets, models, and metrics are composed through declarative YAML configuration files. The framework distinguishes itself through its extensible model integration layer, which supports custom models, HuggingFace models, and third-party API

CVXPY is a Python-embedded domain-specific language for modeling and solving convex optimization problems using natural mathematical syntax. It is built on a disciplined convex programming framework that automatically enforces convexity rules, ensuring that problems formulated by the user are valid for convex solvers. The project also functions as a multi-solver optimization interface, abstracting away backend details and dispatching problems to specialized solvers like ECOS, SCS, and Gurobi without manual configuration. Beyond standard convex optimization, CVXPY extends its reach to geometri

Giskard est un framework d'évaluation, une bibliothèque de test et un système de monitoring de qualité pour les grands modèles de langage (LLM) et les agents IA. Il sert de boîte à outils pour quantifier la performance et la fiabilité des modèles, offrant des capacités spécialisées pour valider les pipelines de génération augmentée par récupération (RAG). Le projet se distingue par un outil de red teaming automatisé et un scanner de sécurité conçu pour identifier les vulnérabilités, les injections de prompts et les risques de sécurité. Il utilise le probing contradictoire et la génération synthétique de cas limites pour quantifier la robustesse du modèle et détecter les fuites d'informations. La plateforme couvre un large éventail de capacités, incluant la détection d'hallucinations et la vérification de l'exactitude factuelle, le benchmarking de la logique et du raisonnement, ainsi que la détection de biais. Elle fournit des outils pour les tests de régression, l'évaluation des composants RAG et la génération automatisée de cas de test à partir de bases de connaissances. Le système inclut des fonctionnalités de gestion pour les espaces de travail collaboratifs, le contrôle d'accès basé sur les rôles et des pipelines d'évaluation planifiés pour surveiller la dérive des performances au fil du temps.

Python-Guide-CN est une traduction chinoise d'un guide complet sur la programmation Python idiomatique et le développement logiciel. Il sert de tutoriel de programmation curé et de référence d'écosystème, fournissant un chemin structuré pour apprendre la syntaxe Python, les bibliothèques standard et les modèles de codage professionnels. Le projet se distingue en offrant des instructions détaillées pour configurer des environnements de développement sous Windows, macOS et Linux. Il se concentre spécifiquement sur la sélection des interpréteurs et la gestion des environnements virtuels pour assurer un espace de travail cohérent. Le guide couvre un large éventail de capacités techniques, y compris les flux de travail de test logiciel, la distribution de paquets et l'implémentation des meilleures pratiques de codage. Il fournit en outre des conseils sur le développement web, la construction d'API REST et le calcul scientifique, y compris l'analyse et la visualisation de données.

This project is a language model evaluation framework and benchmarking tool designed to measure the accuracy and performance of models across diverse datasets. It provides a system for implementing model-based graders, running standardized tests for mathematical reasoning, coding, and factuality, and calculating quantified performance metrics such as precision, recall, F1 scores, and pass-at-k. The framework utilizes model-based grading and rubrics to validate response quality against expert-defined criteria. It includes a multi-model benchmarking loop and a model-agnostic API interface to co

OpenGpt est une plateforme d'orchestration d'agents et une interface multimodale conçue pour construire et déployer des personas IA spécialisés. Elle permet aux utilisateurs de créer des agents orientés vers des tâches avec des invites système personnalisées et des contraintes comportementales pour automatiser les flux de travail professionnels, créatifs et techniques. Le projet propose un flux de travail d'ingénierie de prompt qui transforme les entrées utilisateur simples en instructions structurées pour améliorer la précision du modèle. Il intègre la génération augmentée par récupération (RAG) en connectant des bases de données vectorielles à l'interface de chat, permettant des réponses contextuelles à partir de jeux de données privés. La plateforme couvre un large éventail de capacités, y compris l'analyse de données multimodales pour les PDF et l'audio, la gestion d'API multi-fournisseurs via des clés personnelles, et la génération de divers types de contenu tels que des documents professionnels, du code fonctionnel et des invites visuelles. Elle inclut également des outils pour l'analyse de contenu, les services de traduction et la gestion d'identité via Google OAuth.

simpleRL-reason is a training framework designed to improve mathematical and logical deduction in large language models. It utilizes reinforcement learning and policy optimization to enhance the accuracy and transparency of step-by-step deduction chains. The project implements a pipeline that establishes baseline capabilities through supervised fine-tuning before applying reinforcement learning to maximize deductive accuracy. It features a reward modeling toolkit that calculates scalar feedback by comparing generated reasoning steps against verified mathematical ground truths. The framework

Drake is a robotics simulation framework and control system modeling tool used for designing, simulating, and verifying the dynamics of complex robotic systems. It functions as a multibody dynamics simulator and a mathematical optimization library, providing a suite of algorithms for trajectory optimization and the simulation of articulated robots. The framework is distinguished by its block-diagram system for composing dynamical subsystems and its ability to formulate and solve diverse mathematical programs, including linear, quadratic, and nonconvex nonlinear problems. It supports specializ

Riskfolio-Lib is a Python portfolio optimization library and convex risk management tool. It provides a framework for calculating optimal asset allocations using convex risk measures and mathematical programming solvers, supporting linear, quadratic, and semidefinite programming. The library features a hierarchical risk parity framework and financial asset clustering tools to group similar instruments and improve diversification. It includes a portfolio backtesting engine for simulating investment strategies using historical data and cross-validation. The system covers a broad range of quant

This project is a containerized search infrastructure designed to deploy a privacy-focused metasearch engine. It acts as a self-hosted search proxy that aggregates results from multiple external web, image, and academic search providers while anonymizing requests and stripping trackers to protect user identity. The system utilizes Docker to orchestrate the search instance, integrating caching mechanisms and reverse proxy support to ensure a private and efficient search environment. It employs a modular adapter-based integration to standardize diverse external API responses and a processing pi

DataFlow is an agent-based workflow orchestrator and data pipeline designed to synthesize, clean, and augment large-scale datasets for training large language models. It functions as a synthetic data generator and text curation tool, utilizing an intelligent assistant to assemble modular processing operators into functional pipelines based on user requirements. The project distinguishes itself through a low-code approach, providing a web-based visual interface for designing and monitoring multi-stage execution flows. It features an operator-based registry system that allows for the integratio