Best AI tools for< Build Python Generative Models >

Hal9 is an AI coworker creation platform that allows organizations, data teams, and developers to effortlessly build custom AI coworkers with any level of complexity. It provides a secure and customizable model-agnostic AI coworker solution that accelerates the development of AI applications by saving significant engineering time. Hal9 enables users to leverage the best generative AI models, connect their data securely, and start building enterprise-ready AI applications with the necessary engineering components. The platform aims to empower users to leverage AI technology effectively and efficiently in their projects.

Deepnote is an AI-powered analytics and data science notebook platform designed for teams. It allows users to turn notebooks into powerful data apps and dashboards, combining Python, SQL, R, or even working without writing code at all. With Deepnote, users can query various data sources, generate code, explain code, and create interactive visualizations effortlessly. The platform offers features like collaborative workspaces, scheduling notebooks, deploying APIs, and integrating with popular data warehouses and databases. Deepnote prioritizes security and compliance, providing users with control over data access and encryption. It is loved by a community of data professionals and widely used in universities and by data analysts and scientists.

Floneum is a versatile AI-powered tool designed for language tasks. It offers a user-friendly interface to build workflows using large language models. With Floneum, users can securely extend functionality by writing plugins in various languages compiled to WebAssembly. The tool provides a sandboxed environment for plugins, ensuring limited resource access. With 41 built-in plugins, Floneum simplifies tasks such as text generation, search engine operations, file handling, Python execution, browser automation, and data manipulation.

Athena Intelligence is an AI-native analytics platform and artificial employee designed to accelerate analytics workflows by offering enterprise teams co-pilot and auto-pilot modes. Athena learns your workflow as a co-pilot, allowing you to hand over controls to her for autonomous execution with confidence. With Athena, everyone in your enterprise has access to a data analyst, and she doesn't take days off. Simple integration to your Enterprise Data Warehouse Chat with Athena to query data, generate visualizations, analyze enterprise data and codify workflows. Athena's AI learns from existing documentation, data and analyses, allowing teams to focus on creating new insights. Athena as a platform can be used collaboratively with co-workers or Athena, with over 100 users in the same report or whiteboard environment concurrently making edits. From simple queries and visualizations to complex industry specific workflows, Athena enables you with SQL and Python-based execution environments.

lab2.dev is an AI tool that allows users to generate Python applications using simple text prompts. It helps users, regardless of their coding experience, to quickly turn their ideas into functional Python apps. With lab2 AI, users can easily build generative AI apps and streamline their workflow in app development. The tool provides AI assistance to generate Streamlit apps in minutes, and offers a community gallery to explore various apps created by users. lab2 AI aims to simplify the app development process and empower users to create AI-powered applications effortlessly.

Meya is a chatbot platform that allows users to build and launch custom chatbots. It provides a variety of features, including a visual flow editor, a code editor, and a variety of integrations. Meya is designed to be easy to use, even for non-technical users. It is also highly extensible, allowing users to add their own custom code and integrations.

Replit is a software creation platform that provides an integrated development environment (IDE), artificial intelligence (AI) assistance, and deployment services. It allows users to build, test, and deploy software projects directly from their browser, without the need for local setup or configuration. Replit offers real-time collaboration, code generation, debugging, and autocompletion features powered by AI. It supports multiple programming languages and frameworks, making it suitable for a wide range of development projects.

Trudo.ai is an AI-powered workflow automation platform that allows users to build complex workflows using simple English language commands. The platform is backed by Python code and features interactive UI components. Users can create and customize nodes, handle dynamic routing, and benefit from flexible memory allocation. Trudo.ai also offers AI Copilot functionality for non-technical users to generate logic and user interfaces. With support for various data types and no extra frameworks required, Trudo.ai covers a wide range of use cases and provides versions to track workflow changes.

Numerai is a data science tournament platform where users can compete to build models that predict the stock market. The platform provides users with clean and regularized hedge fund quality data, and users can build models using Python or R scripts. Numerai also has a cryptocurrency, NMR, which users can stake on their models to earn rewards.

Cerebium is a serverless AI infrastructure platform that allows teams to build, test, and deploy AI applications quickly and efficiently. With a focus on speed, performance, and cost optimization, Cerebium offers a range of features and tools to simplify the development and deployment of AI projects. The platform ensures high reliability, security, and compliance while providing real-time logging, cost tracking, and observability tools. Cerebium also offers GPU variety and effortless autoscaling to meet the diverse needs of developers and businesses.

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.

LangChain is a framework for developing applications powered by large language models (LLMs). It simplifies every stage of the LLM application lifecycle, including development, productionization, and deployment. LangChain consists of open-source libraries such as langchain-core, langchain-community, and partner packages. It also includes LangGraph for building stateful agents and LangSmith for debugging and monitoring LLM applications.

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Streamlit is an open-source Python library that makes it easy to create and share beautiful and interactive web apps for data science and machine learning.

Langflow is a low-code app builder for RAG and multi-agent AI applications. It is Python-based and agnostic to any model, API, or database. Langflow offers a visual IDE for building and testing workflows, multi-agent orchestration, free cloud service, observability features, and ecosystem integrations. Users can customize workflows using Python and publish them as APIs or export as Python applications.

Gradio is a tool that allows users to quickly and easily create web-based interfaces for their machine learning models. With Gradio, users can share their models with others, allowing them to interact with and use the models remotely. Gradio is easy to use and can be integrated with any Python library. It can be used to create a variety of different types of interfaces, including those for image classification, natural language processing, and time series analysis.

Supersimple is an AI-native data analytics platform that combines a semantic data modeling layer with the ability to answer ad hoc questions, giving users reliable, consistent data to power their day-to-day work.

Developer Roadmaps (roadmap.sh) is a community-driven platform offering official roadmaps, guides, projects, best practices, questions, and videos to assist developers in skill development and career growth. It provides role-based and skill-based roadmaps covering various technologies and domains. The platform is actively maintained and continuously updated to enhance the learning experience for developers worldwide.

Data Science Dojo is a globally recognized e-learning platform that offers programs in data science, data analytics, machine learning, and more. They provide comprehensive and hands-on training in various formats such as in-person, virtual instructor-led, and self-paced training. The focus is on helping students develop a think-business-first mindset to apply their data science skills effectively in real-world scenarios. With over 2500 enterprises trained, Data Science Dojo aims to make data science accessible to everyone.

Metaflow is an open-source framework for building and managing real-life ML, AI, and data science projects. It makes it easy to use any Python libraries for models and business logic, deploy workflows to production with a single command, track and store variables inside the flow automatically for easy experiment tracking and debugging, and create robust workflows in plain Python. Metaflow is used by hundreds of companies, including Netflix, 23andMe, and Realtor.com.

AI-Toolbox is a C++ library aimed at representing and solving common AI problems, with a focus on MDPs, POMDPs, and related algorithms. It provides an easy-to-use interface that is extensible to many problems while maintaining readable code. The toolbox includes tutorials for beginners in reinforcement learning and offers Python bindings for seamless integration. It features utilities for combinatorics, polytopes, linear programming, sampling, distributions, statistics, belief updating, data structures, logging, seeding, and more. Additionally, it supports bandit/normal games, single agent MDP/stochastic games, single agent POMDP, and factored/joint multi-agent scenarios.

A curated list of 50+ resources to help you become a Generative AI Data Scientist. This repository includes resources on building GenAI applications with Large Language Models (LLMs), and deploying LLMs and GenAI with Cloud-based solutions.

AITreasureBox is a comprehensive collection of AI tools and resources designed to simplify and accelerate the development of AI projects. It provides a wide range of pre-trained models, datasets, and utilities that can be easily integrated into various AI applications. With AITreasureBox, developers can quickly prototype, test, and deploy AI solutions without having to build everything from scratch. Whether you are working on computer vision, natural language processing, or reinforcement learning projects, AITreasureBox has something to offer for everyone. The repository is regularly updated with new tools and resources to keep up with the latest advancements in the field of artificial intelligence.

Generative Models by Stability AI is a repository that provides various generative models for research purposes. It includes models like Stable Video 4D (SV4D) for video synthesis, Stable Video 3D (SV3D) for multi-view synthesis, SDXL-Turbo for text-to-image generation, and more. The repository focuses on modularity and implements a config-driven approach for building and combining submodules. It supports training with PyTorch Lightning and offers inference demos for different models. Users can access pre-trained models like SDXL-base-1.0 and SDXL-refiner-1.0 under a CreativeML Open RAIL++-M license. The codebase also includes tools for invisible watermark detection in generated images.

This repository is a refactored and updated version of `stable-audio-tools`, an open-source code for audio/music generative models originally by Stability AI. It contains refactored codes for improved readability and usability, useful scripts for evaluating and playing with trained models, and instructions on how to train models such as `Stable Audio 2.0`. The repository does not contain any pretrained checkpoints. Requirements include PyTorch 2.0 or later for Flash Attention support and Python 3.8.10 or later for development. The repository provides guidance on installing, building a training environment using Docker or Singularity, logging with Weights & Biases, training configurations, and stages for VAE-GAN and Diffusion Transformer (DiT) training.

Generative AI with LangChain is a code repository for building large language model (LLM) apps with Python, ChatGPT, and other LLMs. The repository provides code examples, instructions, and configurations for creating generative AI applications using the LangChain framework. It covers topics such as setting up the development environment, installing dependencies with Conda or Pip, using Docker for environment setup, and setting API keys securely. The repository also emphasizes stability, code updates, and user engagement through issue reporting and feedback. It aims to empower users to leverage generative AI technologies for tasks like building chatbots, question-answering systems, software development aids, and data analysis applications.

fastRAG is a research framework designed to build and explore efficient retrieval-augmented generative models. It incorporates state-of-the-art Large Language Models (LLMs) and Information Retrieval to empower researchers and developers with a comprehensive tool-set for advancing retrieval augmented generation. The framework is optimized for Intel hardware, customizable, and includes key features such as optimized RAG pipelines, efficient components, and RAG-efficient components like ColBERT and Fusion-in-Decoder (FiD). fastRAG supports various unique components and backends for running LLMs, making it a versatile tool for research and development in the field of retrieval-augmented generation.

Awesome Generative AI & LLM APIs is a curated list of useful APIs that allow developers to integrate generative models into their applications without building the models from scratch. These APIs provide an interface for generating text, images, or other content, and include pre-trained language models for various tasks. The goal of this project is to create a hub for developers to create innovative applications, enhance user experiences, and drive progress in the AI field.

LLMWare is a framework for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows. This project provides a comprehensive set of tools that anyone can use - from a beginner to the most sophisticated AI developer - to rapidly build industrial-grade, knowledge-based enterprise LLM applications. Our specific focus is on making it easy to integrate open source small specialized models and connecting enterprise knowledge safely and securely.

This repository contains notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage generative AI workflows using Generative AI on Google Cloud, powered by Vertex AI. For more Vertex AI samples, please visit the Vertex AI samples Github repository.

biniou is a self-hosted webui for various GenAI (generative artificial intelligence) tasks. It allows users to generate multimedia content using AI models and chatbots on their own computer, even without a dedicated GPU. The tool can work offline once deployed and required models are downloaded. It offers a wide range of features for text, image, audio, video, and 3D object generation and modification. Users can easily manage the tool through a control panel within the webui, with support for various operating systems and CUDA optimization. biniou is powered by Huggingface and Gradio, providing a cross-platform solution for AI content generation.

This repository is a collection of resources focused on interpretability in large language models (LLMs). It aims to help beginners get started in the area and keep researchers updated on the latest progress. It includes libraries, blogs, tutorials, forums, tools, programs, papers, and more related to interpretability in LLMs.

This project provides a proof of concept for deploying a scalable, serverless LLM Generative AI inference engine on AWS Lambda. It leverages the llama.cpp project to enable the usage of more accessible CPU and RAM configurations instead of limited and expensive GPU capabilities. By deploying a container with the llama.cpp converted models onto AWS Lambda, this project offers the advantages of scale, minimizing cost, and maximizing compute availability. The project includes AWS CDK code to create and deploy a Lambda function leveraging your model of choice, with a FastAPI frontend accessible from a Lambda URL. It is important to note that you will need ggml quantized versions of your model and model sizes under 6GB, as your inference RAM requirements cannot exceed 9GB or your Lambda function will fail.

SambaNova AI Starter Kits is a collection of open-source examples and guides designed to facilitate the deployment of AI-driven use cases for developers and enterprises. The kits cover various categories such as Data Ingestion & Preparation, Model Development & Optimization, Intelligent Information Retrieval, and Advanced AI Capabilities. Users can obtain a free API key using SambaNova Cloud or deploy models using SambaStudio. Most examples are written in Python but can be applied to any programming language. The kits provide resources for tasks like text extraction, fine-tuning embeddings, prompt engineering, question-answering, image search, post-call analysis, and more.

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

The 100 Days in AI repository provides a comprehensive roadmap for individuals to learn Artificial Intelligence over a period of 100 days. It covers topics ranging from basic programming in Python to advanced concepts in AI, including machine learning, deep learning, and specialized AI topics. The repository includes daily tasks, resources, and exercises to ensure a structured learning experience. By following this roadmap, users can gain a solid understanding of AI and be prepared to work on real-world AI projects.

是一个专门用于建立数学模型并撰写python代码的gpt.

从Python基础到AI领域的导师

一名使用python语言的，专业的AI工程师

Expert in Alteryx, Power BI, Power Automate, Python, MySQL, Salesforce, & Tableau

Adaptable full-stack dev guide, expert in Python and R.

I'm a Python and PyTorch expert with knowledge of ML infrastructure requirements ready to help you build and scale your ML projects.

Beginner-friendly Python guide including practical projects

An in-depth Python programming guide for all levels.

Asistente y maestro experto en Python, enfocado en la enseñanza y apoyo en proyectos de programación.

A Python and programming expert, guiding users on best practices for writing clean, efficient, and well-documented Python code.

A Python tutor creating stepwise coding challenges.

Provides optimized, commented Python code.

Professional Python expert, quick and to the point

Teaches you how to use the Metaphor Search API using our Python SDK

python 程序开发工程师

Expert in building Python web services on Render.

Expert Backend Developer: Skilled in Python, Java, Node.js, Ruby, PHP for robust backend solutions.

Assists with Python ML projects, offering coding help and best practices.

I am a digital scientist skilled in Python, here to assist with scientific and data analysis tasks.