generative-ai-tools

There are 21 repositories under generative-ai-tools topic.

  • llmware-ai/llmware

    Unified framework for building enterprise RAG pipelines with small, specialized models

    Language:Python8.2k441551.5k

  • felladrin/awesome-ai-web-search

    A list of software that allows searching the web with the assistance of AI.

    Language:HTML4946825

  • teilomillet/gollm

    Unified Go interface for Language Model (LLM) providers. Simplifies LLM integration with flexible prompt management and common task functions.

    Language:Go3038822

  • omnitool-ai/omnitool

    Official Omnitool repository

    Language:TypeScript126628

  • drshahizan/Generative-AI-Playground

    Generative-AI-Playground is a platform for experimenting with different generative models and techniques. It lets you try out advanced technologies like ChatGPT, Bing.AI and Gemini. This playground is a place where people can learn and practice using these models.

    444113

  • Ryota-Kawamura/Generative-AI-for-Everyone

    You’ll get insights into what generative AI can do, its potential, and its limitations. You’ll delve into real-world applications and learn common use cases.

    Language:Jupyter Notebook312013
  • novelai-python

    LlmKira/novelai-python

    ✨ NovelAI api python sdk, easy to use, modern and user-friendly.

    Language:Python272190

  • Rabbia-Hassan/Generative-AI-for-Everyone

    Taught by AI genius Andrew NG, this course entails the cutting edge topics such as, How generative AI works including what it can and can't do, Common uses cases such as Reading, Writing, and Chatting, Life Cycle of GenAI projects, Advanced Technology options such as RAG, Fine tunning, and Pre-Training, Implications of GenAI on business & Society.

    Language:Jupyter Notebook151012
  • insatgram-ai-model

    codewithmuh/insatgram-ai-model

    Create high-quality images effortlessly for your brand using Fooocus, an advanced image generation software.

    9201

  • ArtyLLaMa/ArtyLLaMA

    ArtyLLama is an experimental AI-powered platform for interactive content creation. It integrates with various AI models (Ollama, OpenAI, Anthropic) to explore natural language-driven development of web experiences, 3D visualizations, and more. Features include real-time AI chat, dynamic model selection, and semantic search across chat history.

    Language:JavaScript6140

  • noarche/AutoPrompt

    100+ billion unique prompt. Create random prompts to help with learning better prompting techniques. Works with any AI platform!

    Language:Python5201

  • Ian-Tharp/cognitive-core

    Cognitive CORE is an advanced template for building systems towards achieving Artificial General Intelligence (AGI). It stands for Comprehension, Orchestration, Reasoning, and Evaluation — the four pillars that provide a modular and scalable foundation for autonomous task execution and decision-making using natural language inputs.

    Language:Python3201

  • nitya/prompty

    Prompty makes it easy to create, manage, debug, and evaluate LLM prompts for your AI applications. Prompty is an asset class and format for LLM prompts designed to enhance observability, understandability, and portability for developers.

    Language:Python100

  • rlsn/Tripper

    A stable diffusion pipeline that generates consecutive sequence of images

    Language:Python1100

  • spark-engine-opensource-projects/quantum-computing-application-generator

    Quantum Problem Solver with Circuit Design A Next.js application that allows users to define quantum computing problems and automatically generate quantum circuit designs using the Spark Engine API. Features a dynamic UI, problem type selection, and real-time circuit generation. Deployable on Vercel with serverless functions for API interaction.

    Language:JavaScript1100

  • anirbanbasu/dqa

    LLM agents for multi-hop question answering (MHQA)

    Language:Python0200

  • jvalverr/data-analytics-education

    We compare traditional tools with LLM-based tools to evaluate their effectiveness in data analytics education for non-computational professionals and students. Our approach, validated through two case studies, shows that using generative AI as the primary tool significantly improves learning efficiency and project development speed.

    Language:Jupyter Notebook0100

  • leo-capvano/describe-a-python-project

    Describe each file of a python project by asking a Generative AI model to generate a natural language explanation of each file

    Language:Python0100

  • luluwangyy/AIArtNewsApp

    AI conceptual art and AI art news generator (for Heroku deployment)

    Language:HTML0100

  • ltoscano/keyvault

    A simple key management system for development with LLMs and related cloud services

    Language:Python

  • Pavansomisetty21/Question-Answer-pair-Generation-using-GEMINI-LLM

    In this we generate QA pairs from the paragraph content and pdf content

    Language:Jupyter Notebook10