#Generative AI

Leveraging generative AI, this tool provides a robust RAG framework that is fast, efficient, and customizable. It is capable of integrating with various LLMs and supports multiple file types, facilitating enhanced workflows through internet search capabilities and additional tools. The quivr-core allows easy file ingestion and inquiry, designed to optimize focus on product development. This tool is ideal for those seeking innovative approaches to data retrieval and management.

Explore Ocular's open-source generative AI search platform, designed for easy integration with existing enterprise systems. With features like Google-like search and modular customization, it supports efficient internal search setups. Utilize custom connectors for proprietary data and access popular apps through its marketplace, all within a secure governance framework.

Generative AI Use Cases JP offers secure applications for business transformation through generative AI. The repository includes chat, text generation, and translation solutions using advanced language models to meet business requirements. With browser extensions and AWS integrations, including Amazon Bedrock and Amazon Kendra, processes are streamlined. Discover capabilities like RAG chat and video analysis, demonstrated by companies like Yasashii Te and Salsonido for operational benefits.

LLMStack is a no-code platform designed for developing customized generative AI agents, workflows, and chatbots. The platform allows the integration of data and business processes without requiring coding skills, enabling the chaining of multiple LLMs to construct sophisticated applications. LLMStack supports deployment on both cloud and on-premise infrastructures and offers multi-tenant capabilities, suitable for creating AI solutions such as sales assistants, research analysts, or RPA automations. It includes API access and integration with Slack and Discord for streamlined use.

Utilize AWS CDK to deploy chatbots with a wide range of Large and Multimodal Language Models. This project supports experimenting with different models and configurations, facilitating easy integration with services such as Amazon Bedrock, Amazon SageMaker, and third-party providers like OpenAI and Anthropic. The framework offers a comprehensive solution for building chatbots suited for diverse applications, enhancing interaction capabilities across platforms. Explore secure messaging and AI-powered document processing through additional resources. An open-source library helps developers design AI solutions using pattern-based architecture.

This repository organizes quality AI technology information sources, aiding in knowledge synchronization and closing information gaps. Highlighting platforms like Twitter for timely updates, it offers strategies to filter quality content. The project welcomes contributions to expand resources, covering platforms like Twitter, Zhihu, and academic journals, serving as a detailed guide for those pursuing the latest AI insights.

Uncover a wide range of open-source GitHub repositories centered on ChatGPT. This curated list provides insights into numerous projects such as prompts, CLIs, and NLP tools. With regular updates, it serves as a valuable resource for developers and researchers interested in improving ChatGPT solutions. A search tool is also available on Hugging Face Spaces for easier navigation. Contributions are encouraged, adhering to clear guidelines to maintain quality and uniformity. Stay informed with the latest developments within the ChatGPT ecosystem.

DeepWorks offers a range of in-depth projects and resources in deep learning, AI, and machine learning, featuring workshops on satellite imagery and model exploration, including LLM and NeRF. Enhance your expertise through developer programs in Python and Rust, and explore advanced models such as Stable Diffusion. Accessible guides and tutorials are available on Prodramp's YouTube and GitHub.

Explore a diverse array of resources and scholarly papers on Diffusion Models covering domains such as vision, audio, and natural language. This repository provides comprehensive access to introductory materials, tutorials, and advanced research, aiding in understanding the theory, applications, and developments in Diffusion Models. It acts as a practical guide for researchers, students, and professionals interested in deepening their knowledge on Diffusion Models, featuring practical implementations, comprehensive surveys, and instructional content.

Access a range of resources and insights from the Generative AI repository, designed for users at all levels. The project features a structured repository with daily video content, a glossary of essential terms, a roadmap, and interview preparation resources. Deepen your understanding of Generative AI through practical resources such as embedding models, AWS and Azure cloud integrations, and advanced decision flow charts. Whether investigating conversational analytics or working with Langchain and large language models, these resources support continuous learning in generative AI.

The Abel project showcases an advanced AI model that significantly surpasses traditional methods on mathematical datasets. Utilizing a distinctive 'Parental Oversight' strategy, the model achieves leading results in problem-solving benchmarks without relying on external tools, emphasizing the untapped potential of supervised fine-tuning. This approach delivers superior mathematical reasoning and adaptability across diverse datasets.

Discover a collection of Jupyter notebooks for machine learning, deep learning, and analytics, covering topics such as time series forecasting, computer vision, and anomaly detection. Explore distributed computing, generative AI, and techniques like hyperparameter tuning and recommender systems. Each notebook provides detailed examples, supporting data scientists and AI enthusiasts in applying Python to real-world business challenges.

Improve interview skills with an application designed for real-time audio processing, voice recognition, and response generation using Generative AI. The tool transcribes and answers questions with OpenAI's Whisper and GPT models, offering cross-platform capability. Ideal for practice and learning, it includes an intuitive interface and MacOS audio support via BlackHole. This proof-of-concept requires Python 3.10+ and an OpenAI API key for setup. Ethical use is strongly recommended.

Garak identifies vulnerabilities in large language models (LLMs) such as hallucination and data leakage through various testing methods. It supports multiple platforms including Hugging Face, OpenAI, and REST. Designed for ease of use, it can be installed on Linux and OSX via pip or source cloning. Garak helps developers maintain the integrity of AI systems and ensure reliable user interactions. Join its active community on Discord for support and insights.

Explore a curated collection of over 2435 generative AI applications across 43 categories. Ranging from tools like AIApply for job applications to creative platforms like CreativePixel, these applications enhance creativity, automation, and productivity. This comprehensive resource supports diverse needs, including marketing, job searching, and content creation. Stay updated in the evolving AI landscape and discover tools to facilitate growth and efficiency.

NVIDIA's NeMo Framework facilitates the creation of large language models and speech recognition systems with modular design and ease of use. It offers updated support for Llama 3.1 LLMs and enhances AI training with compatibility for Amazon EKS and Google Kubernetes Engine, while delivering significant improvements in ASR model inference speeds and multilingual capabilities through the Canary model.

scGPT employs Generative AI to advance Single-Cell Multi-omics models, featuring pretrained models and zero-shot tutorials for tasks such as cell embedding and reference mapping. It integrates seamlessly with CPU and GPU environments thanks to its flash-attention dependency option. Recent updates enhance its efficiency in handling extensive datasets like CellXGene. Available via PyPI, scGPT invites community contributions for ongoing development. Explore its capabilities with superbio.ai-supported online applications and fine-tune models for scRNA-seq tasks.

Cover Agent, developed by CodiumAI, uses Generative AI to improve code coverage through automated test generation. As part of a utility suite, it helps streamline development workflows with Docker and multi-language support, extensive database logging, and integration with various Continuous Integration platforms. Recent updates include enhanced system diagrams, comprehensive documentation, and improved CI/CD pipelines for better error handling and test generation.

KoGPT by KakaoBrain is a Korean generative pre-trained transformer designed for tasks such as classification, search, summarization, and generation of Korean text. It features over 6 billion parameters with 28 layers, requiring at least 32GB of GPU RAM for optimal functioning. Available in various precision formats, including float16, this reduces memory use. Users should be aware of the potential generation of sensitive content due to training on raw data. Learn more about its specifications for integration into AI applications.

Discover a wide range of Generative AI projects that produce innovative content in art, marketing, and academia. The repository includes advanced models creating photorealistic images, digital art, music, and more, which are often indistinguishable from human-made works. It also offers comprehensive contribution guidelines and highlights projects in new areas, serving as a valuable resource for developers and enthusiasts eager to learn more about Generative AI advancements.

Discover Jupyter notebooks demonstrating processes to build, train, and deploy models with Amazon SageMaker. These cover tasks like data preparation, model construction, and deployment, plus advanced features such as MLOps and generative AI. Suitable for ML experts aiming to utilize SageMaker's services, these resources include detailed documentation and code samples for easy integration into diverse workflows.

NeMo Curator is a GPU-optimized open-source library designed to speed up dataset preparation in generative AI contexts. Utilizing Dask and RAPIDS, it provides efficient modules for curating multilingual text and images, thereby enhancing training and tuning processes. Features such as language identification, filtering, and deduplication support various AI tasks, including pretraining and fine-tuning. Its modular approach allows for the customization of data workflows while maintaining objectivity and clarity.

Explore a carefully curated archive of top AI tools and models aimed at boosting creativity and productivity. Our extensive database includes generative AI and LLMs, keeping users abreast of the latest advancements via regular Altern Newsletter updates. Discover diverse functions like AI text, code, images, audio, marketing tools, and learning materials that support professionals across various fields. Contribute or feature AI tools within a collaborative community focused on advancing innovation.

Utilize EdgeChains to streamline your generative AI deployments with a focus on stability and scalability. As a platform developed on Google's jsonnet and Cloudflare's honojs, it provides a reliable framework for production-ready GenAI applications with minimal effort. EdgeChains is crafted for fault tolerance, automatic task parallelization, and scalable operations, making it ideal for extensive datasets and diverse APIs. The versionable prompts simplify prompt engineering and manage changes over time. It also allows detailed tracking of token costs and assures testability, making it a practical choice for developers.

This course imparts key techniques for implementing Generative AI in cloud-based systems. Participants explore the evolving developer role and AI technology integration under the guidance of industry experts. The curriculum emphasizes practical skills, including scalable architecture, enterprise system integration, and prompt engineering validation, equipping learners for an AI-centric future. Gain insights into the societal and business implications of Generative AI and learn to foster technological progress.

Learn to utilize foundation models with Amazon Bedrock in a developer-focused workshop offering hands-on labs in text and image generation, model customization, and knowledge bases. The workshop demonstrates practical applications to increase productivity, such as text summarization and chatbot development, and includes integration options with open-source tools like LangChain and FAISS. Suitable for use in environments including SageMaker Studio, this guide offers comprehensive insights into effective AI solution implementation.

Alan AI provides a comprehensive solution for integrating Generative AI Agents into web applications, minimizing UI changes and enhancing workflow efficiency. The platform includes tools like Alan AI Studio for dialog management and analytics, lightweight SDKs for easy integration, and a supportive cloud environment. Its serverless architecture optimizes infrastructure use, facilitating quick updates and management. Alan AI supports multiple frameworks, including React, Angular, and Vue, ensuring adaptable implementation for diverse app requirements.

This GitHub repository offers a set of pre-built examples to help users get started with Amazon Bedrock efficiently. It includes resources on Bedrock basics, prompt engineering, generative AI agent implementation, and custom model integration. Additionally, it covers working with multimodal data, use cases for generative AI, and implementing RAG. The repository highlights responsible AI practices and guides from proof of concept to production deployment, with each folder offering detailed instructions.

Superagent, an open-source framework, enables seamless AI assistant integration into applications using advanced models and technologies. It offers chatbot support, content generation, data aggregation, and workflow automation. Developers benefit from REST API access, Python and Typescript SDKs, and features like memory and streaming. Supported by Y Combinator, Superagent provides extensive documentation, making it a versatile tool for creating intelligent applications. Explore it further with tutorials, demos, and community involvement.

FedML provides an extensive solution to efficiently manage AI workloads across decentralized GPUs, multi-cloud environments, and edge servers. Utilizing TensorOpera AI, this unified and scalable machine learning library streamlines model training, deployment, and federated learning. Features like TensorOpera Launch simplify environment management by aligning AI tasks with economical GPU resources. FedML supports use cases such as on-device training and cross-cloud deployments, offering comprehensive MLOps capabilities with TensorOpera Studio and Job Store for smooth execution of AI tasks. It capitalizes on serverless deployments and vector database searches to operate at various scales.

LMOps is a research initiative dedicated to developing AI solutions with foundation models, focusing on enhancing capabilities through Large Language Models (LLMs) and Generative AI. It explores areas such as automatic prompt optimization, extensible prompts, structured prompting, and LLM inference acceleration. The project addresses technologies like context demonstration selection and instruction tuning, improving the functionality and customizability of AI solutions across domains. By providing insights into in-context learning, LMOps significantly contributes to the advancement of AI technologies.

The project demonstrates Java applications enhanced by Generative AI and Large Language Models (LLMs) using Spring AI. It covers use cases such as chatbots, question-answering, semantic search, and text classification with APIs like Ollama, OpenAI, and PGVector. It includes functionalities like structured output, multimodality, function-calling, and embedding models. Resources offer insights into data ingestion, observability, and evaluation, promoting an advanced AI-driven Java development approach.

Develop AI applications effortlessly with the low-code toolkit, using LlamaIndex and leveraging Large Language Models on Open Telekom Cloud. Tailor bots and agents for specific tasks with ease. It features a Next.js front-end and a Python FastAPI backend for data-driven interaction. Utilize this tool to build chat interfaces or connect with diverse data sources. Begin quickly with a T-Systems API key and enhance the speed and adaptability of AI projects, conforming to high coding and licensing principles.

MathPile provides a 9.5 billion token pretraining corpus dedicated to mathematical content, emphasizing diversity and quality. The sources include textbooks, arXiv, Wikipedia, and more, covering all educational levels and competitions. Through rigorous data processing and adherence to licensing requirements, MathPile aids in advancing math-focused AI models by enhancing mathematical reasoning capabilities.

Explore a curated collection of scholarly articles on diffusion models in medical imaging, featuring survey papers, challenges, and applications including anomaly detection and image restoration. This project compiles influential publications from conferences and journals like Medical Image Analysis and MICCAI 2023, serving as a valuable resource for professionals seeking the latest advancements in diffusion model applications.

Explore a comprehensive repository for AI aficionados, featuring top selections in areas including chatbots, search engines, writing tools, and image generators. This collection, attentively curated, benefits both experienced developers and those new to the AI sector, with diverse tools for AI models, video editing, music generation, and autonomous AI innovations. Remain updated with the latest advancements in AI, facilitating a seamless transition into generative AI.

ShipFast provides an open-source SaaS framework ideal for Generative AI and language model projects, aimed at accelerating startup timelines. It includes essential integrations for account and subscription management, content management with Contentful, and features like OpenAI API integration. Future enhancements are planned, such as AI-driven writing and image generation tools. Using React and Django, alongside AWS for infrastructure, the platform offers a solid base for developing AI-focused applications.

Explore Sora, OpenAI's AI model that generates 60-second videos from text. Not publicly available, Sora delivers visually engaging scenes with intricate camera movements and diverse characters. Access resources like GitHub projects, articles, and research papers to understand Sora's impact in fields like film and virtual reality. Keep informed on Sora's growing repository of advancements.

Access a plethora of learning resources around Generative AI on Google Cloud's Vertex AI, including notebooks and code samples for developing and managing generative AI workflows. This extensive repository highlights the use of Vertex AI for creating solutions in image, speech processing, and conversational models.

Explore insights into developing essential software engineering skills with an emphasis on Python and generative AI. Updated monthly, this project explores key competencies in areas like data structures, algorithms, Linux, version control, database management, backend development, system design, frontend basics, and specialized fields such as machine learning and NLP. Designed for individuals aiming to enhance expertise in adjacent technologies methodically, from Python data tools to advanced AI techniques.

Discover innovative approaches in prompt engineering with the Learn Prompting project, featuring expertise in generative AI and LLMs. Benefit from practical guides, content contributions, and a thriving community for AI learning. Acquire hands-on local development skills and gain insights from diverse contributions, maintaining a leading edge in AI innovation.

Explore AI integration with Obsidian for efficient knowledge creation. The open-source Text Generator Plugin facilitates idea generation and content summarization through versatile templates and prompts. Leverage community templates and configure with AI services like Google Generative AI and OpenAI to optimize your personal knowledge management.

Access detailed guides and tutorials on the Generative AI developer site to explore the capabilities of the Gemini API. Utilize resources like notebooks, demos, and code examples to deepen your knowledge of generative AI. Suitable for developers interested in contributing or reporting issues through GitHub, this site provides essential insights and tools. Information available under the Apache License 2.0.

This comprehensive resource lists tools, datasets, and models for Large Language Model (LLM) evaluation, capturing the breadth of Generative AI capabilities. It is a central information point for researchers and developers to access state-of-the-art tools from Hugging Face, OpenAI, and Google. Features include performance metrics for inference speed and multi-modal evaluations, fostering transparent, community-driven advancements in AI assessment approaches.

A detailed review of notable AI advancements made in 2022, with an emphasis on ethical standards, governance models, and transformative applications. Offering insights on the evolution of AI, this review highlights key innovations expected to improve quality of life and calls for conscientious technology application. Includes video explanations and links to scholarly articles and code repositories for a comprehensive understanding of current AI developments.

Explore a PHP framework designed for generative AI applications, compatible with Symfony and Laravel. Supporting major AI models like OpenAI and Anthropic, it facilitates advanced use cases, including chatbots and personalized content creation. Easily installed via Composer and supported by industry sponsors AGO and Theodo.

The repository provides a wide array of audio datasets, such as speech, music, and sound effects, that are crucial for training AI models and advancing AI-generated content. Supporting speech recognition, TTS systems, and research in emotion and language translation, these datasets accommodate various languages and demographics, suitable for both academic and commercial applications. Discover resources from multi-speaker corpora to diverse multilingual speech translation datasets, designed to promote innovation in audio AI technologies.

Explore how ChatGPT and GPT models transform writing, research, and information processing in 'Generative AI with LangChain'. This guide offers practical use cases and detailed insights into leveraging the LangChain framework for building strong LLM applications in areas like customer support and software development. It covers key topics like fine-tuning, prompt engineering, and deployment strategies, along with understanding transformer models, data analysis automation with Python, and chatbot creation. The book underscores maintaining privacy with open-source LLMs. Ideal for developers interested in utilizing generative AI, it helps build innovative solutions, with the repository continually updated in sync with LangChain's progress.

This guide consolidates generative AI work from courses and projects, covering tools like LangChain and Autogen. It outlines practical use cases of LLMs, such as search and data generation, and features sections for learning, tool experimentation, and insightful blog discussions.

Examine how generative AI and deep learning revolutionize molecular and material design. This resource dives into drug design, molecular conformation, and employs AI models like GANs and VAEs. Access datasets, benchmarks, and reviews illustrating the partnership between AI and chemistry for advancements in drug discovery, material science, and conformation analysis.