Best AI tools for< Mathematical Modeler >

Math Sniper is an AI-powered application designed to provide precise math solutions, exam preparation assistance, and exploration of mathematical concepts. The app offers step-by-step solutions to math challenges at all levels, connects users with math tutors for personalized help, and covers a wide range of subjects beyond mathematics, such as biology, chemistry, physics, history, economics, and language tasks. With features like Snap & Ask for instant answers, step-by-step explanations, and a user-friendly interface, Math Sniper aims to enhance users' understanding of complex concepts and facilitate learning in various disciplines.

MIRI (Machine Intelligence Research Institute) is a non-profit research organization dedicated to ensuring that artificial intelligence has a positive impact on humanity. MIRI conducts foundational mathematical research on topics such as decision theory, game theory, and reinforcement learning, with the goal of developing new insights into how to build safe and beneficial AI systems.

Duckietown is a platform for delivering cutting-edge robotics and AI learning experiences. It offers teaching resources to instructors, hands-on activities to learners, an accessible research platform to researchers, and a state-of-the-art ecosystem for professional training. Duckietown's mission is to make robotics and AI education state-of-the-art, hands-on, and accessible to all.

MathGPT is an AI math solver and calculator that provides users with the ability to solve various mathematical problems, including calculations, derivatives, and integrations. It also offers a question notebook feature and AI tutoring capabilities. Users can input mathematical expressions and equations, and MathGPT will provide step-by-step solutions and answers. The tool supports a wide range of mathematical functions and constants, making it a versatile and efficient tool for students, educators, and anyone needing assistance with math problems.

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and high-level mathematical functions to perform operations on these arrays. It is the fundamental package for scientific computing with Python and is used in a wide range of applications, including data science, machine learning, and image processing. NumPy is open source and distributed under a liberal BSD license, and is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

Qwen is an AI tool that focuses on developing and releasing various language models, including dense models, coding models, mathematical models, and vision language models. The Qwen family offers open-source models with different parameter ranges to cater to various user needs, such as production use, mobile applications, coding assistance, mathematical problem-solving, and visual understanding of images and videos. Qwen aims to enhance intelligence and provide smarter and more knowledgeable models for developers and users.

Moogle is a semantic search tool designed for mathlib4, allowing users to find theorems quickly and efficiently. It provides a user-friendly interface for searching mathematical concepts and theorems within the mathlib4 database. Moogle streamlines the process of theorem discovery by leveraging semantic search technology, making it a valuable resource for mathematicians, researchers, and students alike.

neoSVG is an AI-powered application that allows users to generate scalable vector graphics (SVGs) from simple text prompts. The platform utilizes state-of-the-art AI technology and Bezier curve paths with mathematical rules to create unique and high-resolution vector outputs. Users can create a variety of visuals for web design, mobile apps, print media, AR/VR applications, UI/UX designs, logos, and more. neoSVG aims to provide efficient SVG generation by leveraging powerful servers and continuous research to enhance its AI capabilities.

Schemawriter.ai is an advanced AI software platform that generates optimized schema and content on autopilot. It uses a large number of external APIs, including several Google APIs, and complex mathematical algorithms to produce entity lists and content correlated with high rankings in Google. The platform connects directly to Wikipedia and Wikidata via APIs to deliver accurate information about content and entities on webpages. Schemawriter.ai simplifies the process of editing schema, generating advanced schema files, and optimizing webpage content for fast and permanent on-page SEO optimization.

OpenAI Strawberry Model is a cutting-edge AI initiative that represents a significant leap in AI capabilities, focusing on enhancing reasoning, problem-solving, and complex task execution. It aims to improve AI's ability to handle mathematical problems, programming tasks, and deep research, including long-term planning and action. The project showcases advancements in AI safety and aims to reduce errors in AI responses by generating high-quality synthetic data for training future models. Strawberry is designed to achieve human-like reasoning and is expected to play a crucial role in the development of OpenAI's next major model, codenamed 'Orion.'

Math.now is a free online math AI solver powered by Math GPT, offering instant, step-by-step solutions for a wide range of mathematical problems. Users can input math problems or upload photos for analysis, interact with the math AI bot for explanations, and receive real-time assistance. The application supports algebra, geometry, calculus, and word problems, providing detailed guidance and personalized learning experiences. Math.now's AI solver ensures accuracy, efficiency, and accessibility for students, educators, and self-learners.

Vectorizer.io is an online tool that converts raster images (such as PNGs, BMPs, and JPEGs) into scalable vector graphics (SVGs, EPSs, and DXFs). Vectorization is the process of converting pixel-based images into mathematical equations that define lines, curves, and shapes. This makes vector images resolution-independent, meaning they can be scaled to any size without losing quality. Vectorizer.io uses advanced algorithms to accurately trace the outlines of objects in raster images, producing high-quality vector outputs that are suitable for a variety of purposes, such as logo design, web graphics, and print production.

FaceSymAI is an online tool that utilizes advanced AI algorithms to analyze and determine the symmetry of your face. By uploading a photo, the AI examines your facial features, including the eyes, nose, mouth, and overall structure, to provide an accurate assessment of your facial symmetry. The analysis is based on mathematical and statistical methods, ensuring reliable and precise results. FaceSymAI is designed to be user-friendly and accessible, offering a free service to everyone. The uploaded photos are treated with utmost confidentiality and are not stored or used for any other purpose, ensuring your privacy is respected.

Lily AI is an e-commerce product discovery platform that helps brands increase sales and improve customer experience. It uses artificial intelligence to understand the language of customers and inject it across the retail ecosystem, from search to recommendations to demand forecasting. Lily AI's platform is purpose-built for retail and turns qualitative product attributes into a universal, customer-centered mathematical language with unprecedented accuracy. This results in a depth and scale of attribution that no other solution can match.

Mathly is an AI-powered homework help tool that enables students to learn in a better, smarter, and faster way. By using AI technology, Mathly can solve math problems, provide explanations, generate practice problems, and personalize learning experiences based on the user's learning style. It aims to revolutionize the way students approach homework and understanding mathematical concepts.

DeepSeek v3 is an advanced AI language model that represents a major breakthrough in AI language models. It features a groundbreaking Mixture-of-Experts (MoE) architecture with 671B total parameters, delivering state-of-the-art performance across various benchmarks while maintaining efficient inference capabilities. DeepSeek v3 is pre-trained on 14.8 trillion high-quality tokens and excels in tasks such as text generation, code completion, and mathematical reasoning. With a 128K context window and advanced Multi-Token Prediction, DeepSeek v3 sets new standards in AI language modeling.

CogPrints is an electronic archive for self-archived papers in any area of Psychology, Neuroscience, and Linguistics, and many areas of Computer Science (e.g., artificial intelligence, robotics, vision, learning, speech, neural networks), Philosophy (e.g., mind, language, knowledge, science, logic), Biology (e.g., ethology, behavioral ecology, sociobiology, behavior genetics, evolutionary theory), Medicine (e.g., Psychiatry, Neurology, human genetics, Imaging), Anthropology (e.g., primatology, cognitive ethnology, archeology, paleontology), as well as any other portions of the physical, social and mathematical sciences that are pertinent to the study of cognition.

AI Math is an online math solver that uses artificial intelligence to help users solve math problems. It can solve a wide range of math problems, including arithmetic, algebra, geometry, trigonometry, calculus, combinations, word problems, statistics, and probability. AI Math is available in over 30 languages and is free to use. It is a valuable tool for students, educators, and anyone who needs help with math.

Wolfram is a comprehensive platform that unifies algorithms, data, notebooks, linguistics, and deployment to provide a powerful computation platform. It offers a range of products and services for various industries, including education, engineering, science, and technology. Wolfram is known for its revolutionary knowledge-based programming language, Wolfram Language, and its flagship product Wolfram|Alpha, a computational knowledge engine. The platform also includes Wolfram Cloud for cloud-based services, Wolfram Engine for software implementation, and Wolfram Data Framework for real-world data analysis.

Interactive Mathematics is an online platform that provides math problem-solving help, tutoring, and lessons. It offers an AI-powered math problem solver that provides step-by-step answers to math homework problems. The platform also offers on-demand math tutoring, where students can send their math problems to tutors and receive immediate help. Interactive Mathematics also provides a variety of math lessons, covering topics from basic algebra to calculus. The platform is designed to help students improve their math grades and understanding.

PURE (Process-sUpervised Reinforcement lEarning) is a framework that trains a Process Reward Model (PRM) on a dataset and fine-tunes a language model to achieve state-of-the-art mathematical reasoning capabilities. It uses a novel credit assignment method to calculate return and supports multiple reward types. The final model outperforms existing methods with minimal RL data or compute resources, achieving high accuracy on various benchmarks. The tool addresses reward hacking issues and aims to enhance long-range decision-making and reasoning tasks using large language models.

InternLM is a powerful language model series with features such as 200K context window for long-context tasks, outstanding comprehensive performance in reasoning, math, code, chat experience, instruction following, and creative writing, code interpreter & data analysis capabilities, and stronger tool utilization capabilities. It offers models in sizes of 7B and 20B, suitable for research and complex scenarios. The models are recommended for various applications and exhibit better performance than previous generations. InternLM models may match or surpass other open-source models like ChatGPT. The tool has been evaluated on various datasets and has shown superior performance in multiple tasks. It requires Python >= 3.8, PyTorch >= 1.12.0, and Transformers >= 4.34 for usage. InternLM can be used for tasks like chat, agent applications, fine-tuning, deployment, and long-context inference.

LLaMA Factory is a unified framework for fine-tuning 100+ large language models (LLMs) with various methods, including pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO. It features integrated algorithms like GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning, as well as practical tricks like FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA. LLaMA Factory provides experiment monitors like LlamaBoard, TensorBoard, Wandb, MLflow, etc., and supports faster inference with OpenAI-style API, Gradio UI and CLI with vLLM worker. Compared to ChatGLM's P-Tuning, LLaMA Factory's LoRA tuning offers up to 3.7 times faster training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

This repository, Online RLHF, focuses on aligning large language models (LLMs) through online iterative Reinforcement Learning from Human Feedback (RLHF). It aims to bridge the gap in existing open-source RLHF projects by providing a detailed recipe for online iterative RLHF. The workflow presented here has shown to outperform offline counterparts in recent LLM literature, achieving comparable or better results than LLaMA3-8B-instruct using only open-source data. The repository includes model releases for SFT, Reward model, and RLHF model, along with installation instructions for both inference and training environments. Users can follow step-by-step guidance for supervised fine-tuning, reward modeling, data generation, data annotation, and training, ultimately enabling iterative training to run automatically.

This repository contains frontier research on explainable AI (XAI), a hot topic in the field of artificial intelligence. It includes trends, use cases, survey papers, books, open courses, papers, and Python libraries related to XAI. The repository aims to organize and categorize publications on XAI, provide evaluation methods, and list various Python libraries for explainable AI.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

This repository contains the training and inference code needed to replicate the winning solution to the AI Mathematical Olympiad - Progress Prize 1. It consists of fine-tuning DeepSeekMath-Base 7B, high-quality training datasets, a self-consistency decoding algorithm, and carefully chosen validation sets. The training methodology involves Chain of Thought (CoT) and Tool Integrated Reasoning (TIR) training stages. Two datasets, NuminaMath-CoT and NuminaMath-TIR, were used to fine-tune the models. The models were trained using open-source libraries like TRL, PyTorch, vLLM, and DeepSpeed. Post-training quantization to 8-bit precision was done to improve performance on Kaggle's T4 GPUs. The project structure includes scripts for training, quantization, and inference, along with necessary installation instructions and hardware/software specifications.

MathEval is a benchmark designed for evaluating the mathematical capabilities of large models. It includes over 20 evaluation datasets covering various mathematical domains with more than 30,000 math problems. The goal is to assess the performance of large models across different difficulty levels and mathematical subfields. MathEval serves as a reliable reference for comparing mathematical abilities among large models and offers guidance on enhancing their mathematical capabilities in the future.

PyTensor is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It provides the computational backend for `PyMC `__. PyTensor is a fork of `Aesara `__, which is a fork of `Theano `__. PyTensor maintains a static graph which can be modified in-place to allow for advanced optimizations. Contrary to PyTorch and TensorFlow, PyTensor maintains a static graph which can be modified in-place to allow for advanced optimizations.

MathCoder is a repository focused on enhancing mathematical reasoning by fine-tuning open-source language models to use code for modeling and deriving math equations. It introduces MathCodeInstruct dataset with solutions interleaving natural language, code, and execution results. The repository provides MathCoder models capable of generating code-based solutions for challenging math problems, achieving state-of-the-art scores on MATH and GSM8K datasets. It offers tools for model deployment, inference, and evaluation, along with a citation for referencing the work.

This repository provides resources and materials for learning fundamental mathematical concepts essential for artificial intelligence, including linear algebra, calculus, and LaTeX. It includes lecture notes, video playlists, books, and practical sessions to help users grasp key concepts. The repository aims to equip individuals with the necessary mathematical foundation to excel in machine learning and AI-related fields.

AutoMathText is an extensive dataset of around 200 GB of mathematical texts autonomously selected by the language model Qwen-72B. It aims to facilitate research in mathematics and artificial intelligence, serve as an educational tool for learning complex mathematical concepts, and provide a foundation for developing AI models specialized in processing mathematical content.

OREAL is a reinforcement learning framework designed for mathematical reasoning tasks, aiming to achieve optimal performance through outcome reward-based learning. The framework utilizes behavior cloning, reshaping rewards, and token-level reward models to address challenges in sparse rewards and partial correctness. OREAL has achieved significant results, with a 7B model reaching 94.0 pass@1 accuracy on MATH-500 and surpassing previous 32B models. The tool provides training tutorials and Hugging Face model repositories for easy access and implementation.

MathVerse is an all-around visual math benchmark designed to evaluate the capabilities of Multi-modal Large Language Models (MLLMs) in visual math problem-solving. It collects high-quality math problems with diagrams to assess how well MLLMs can understand visual diagrams for mathematical reasoning. The benchmark includes 2,612 problems transformed into six versions each, contributing to 15K test samples. It also introduces a Chain-of-Thought (CoT) Evaluation strategy for fine-grained assessment of output answers.

A mentor in analysis, linking maths to real-world applications, with follow-up questions for deeper understanding.

精通数学分析和高等代数，使用中文回答。

Mathematician and writer in the style of Morris Hirsch.

Generates mathematical conjectures with precision and creativity.

Expert in generating and explaining mathematical, chemical, and computational formulas.

Specialist in numerical methods and mathematical analysis.

ML/DL expert focused on mathematical modeling, Kaggle competitions, and advanced ML models.

Powered by GPT-4 Turbo. 128,000 Tokens. Knowledge base of Discrete Math concepts, proofs and terminology. This GPT is instructed to carefully read and understand the prompt, plan a strategy to solve the problem, and write formal mathematical proofs.

是一个专门用于建立数学模型并撰写python代码的gpt.

解答数理逻辑问题的专家

OCamlは至高の言語

清华大学级别的经济数学建模中文教师。

Precision-focused Language Model for Discrete Mathematics, ensuring unmatched accuracy and error avoidance.

Expert in 3D geometry, physics, engineering, and more.

William Paul Thurston (October 30, 1946 – August 21, 2012) was an American mathematician. He was a pioneer in the field of low-dimensional topology and was awarded the Fields Medal in 1982 for his contributions to the study of 3-manifolds.

Mathematics and Programming genius. Musical Maestro

Expert in Mayan mathematics, focusing on practical applications.

Tu (segundo) mejor profesor de matemáticas discretas.

Your (second) best discrete mathematics teacher. (Now in English)

Expert in financial mathematics, providing detailed explanations and guidance.