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.

Ant Design X is a tool for crafting AI-driven interfaces effortlessly. It is built on the best practices of enterprise-level AI products, offering flexible and diverse atomic components for various AI dialogue scenarios. The tool provides out-of-the-box model integration with inference services compatible with OpenAI standards. It also enables efficient management of conversation data flows, supports rich template options, complete TypeScript support, and advanced theme customization. Ant Design X is designed to enhance development efficiency and deliver exceptional AI interaction experiences.

LangGraph for Java is a library designed for building stateful, multi-agent applications with LLMs. It is a porting of the original LangGraph from the LangChain AI project to Java. The library allows users to define agent states, nodes, and edges in a graph structure to create complex workflows. It integrates with LangChain4j and provides tools for executing actions based on agent decisions. LangGraph for Java enables users to create asynchronous node actions, conditional edges, and normal edges to model decision-making processes in applications.

Bark.cpp is a C/C++ implementation of the Bark model, a real-time, multilingual text-to-speech generation model. It supports AVX, AVX2, and AVX512 for x86 architectures, and is compatible with both CPU and GPU backends. Bark.cpp also supports mixed F16/F32 precision and 4-bit, 5-bit, and 8-bit integer quantization. It can be used to generate realistic-sounding audio from text prompts.

HuixiangDou is a **group chat** assistant based on LLM (Large Language Model). Advantages: 1. Design a two-stage pipeline of rejection and response to cope with group chat scenario, answer user questions without message flooding, see arxiv2401.08772 2. Low cost, requiring only 1.5GB memory and no need for training 3. Offers a complete suite of Web, Android, and pipeline source code, which is industrial-grade and commercially viable Check out the scenes in which HuixiangDou are running and join WeChat Group to try AI assistant inside. If this helps you, please give it a star ⭐

Claude Code Neovim Plugin is a seamless integration between Claude Code AI assistant and Neovim. It allows users to toggle Claude Code in a terminal window with a single key press, automatically detect and reload files modified by Claude Code, provide real-time buffer updates when files are changed externally, offer customizable window position and size, integrate with which-key, use git project root as working directory, maintain a modular code structure, provide type annotations with LuaCATS for better IDE support, offer configuration validation, and include a testing framework for reliability. The plugin creates a terminal buffer running the Claude Code CLI, sets up autocommands to detect file changes on disk, automatically reloads files modified by Claude Code, provides keymaps and commands for toggling the terminal, and detects git repositories to set the working directory to the git root.

Gemini PHP is a PHP API client for interacting with the Gemini AI API. It allows users to generate content, chat, count tokens, configure models, embed resources, list models, get model information, troubleshoot timeouts, and test API responses. The client supports various features such as text-only input, text-and-image input, multi-turn conversations, streaming content generation, token counting, model configuration, and embedding techniques. Users can interact with Gemini's API to perform tasks related to natural language generation and text analysis.

ChatTTS is a generative speech model optimized for dialogue scenarios, providing natural and expressive speech synthesis with fine-grained control over prosodic features. It supports multiple speakers and surpasses most open-source TTS models in terms of prosody. The model is trained with 100,000+ hours of Chinese and English audio data, and the open-source version on HuggingFace is a 40,000-hour pre-trained model without SFT. The roadmap includes open-sourcing additional features like VQ encoder, multi-emotion control, and streaming audio generation. The tool is intended for academic and research use only, with precautions taken to limit potential misuse.

Ollama4j is a Java library that serves as a wrapper or binding for the Ollama server. It facilitates communication with the Ollama server and provides models for deployment. The tool requires Java 11 or higher and can be installed locally or via Docker. Users can integrate Ollama4j into Maven projects by adding the specified dependency. The tool offers API specifications and supports various development tasks such as building, running unit tests, and integration tests. Releases are automated through GitHub Actions CI workflow. Areas of improvement include adhering to Java naming conventions, updating deprecated code, implementing logging, using lombok, and enhancing request body creation. Contributions to the project are encouraged, whether reporting bugs, suggesting enhancements, or contributing code.

ChatGPT Next Web is a well-designed cross-platform ChatGPT web UI tool that supports Claude, GPT4, and Gemini Pro models. It allows users to deploy their private ChatGPT applications with ease. The tool offers features like one-click deployment, compact client for Linux/Windows/MacOS, compatibility with self-deployed LLMs, privacy-first approach with local data storage, markdown support, responsive design, fast loading speed, prompt templates, awesome prompts, chat history compression, multilingual support, and more.

This project aims to create a RESTful API server compatible with the OpenAI API using open-source backends like llama/llama2. With this project, various GPT tools/frameworks can be compatible with your own model. Key features include: - **Compatibility with OpenAI API**: The API server follows the OpenAI API structure, allowing seamless integration with existing tools and frameworks. - **Support for Multiple Backends**: The server supports both llama.cpp and pyllama backends, providing flexibility in model selection. - **Customization Options**: Users can configure model parameters such as temperature, top_p, and top_k to fine-tune the model's behavior. - **Batch Processing**: The API supports batch processing for embeddings, enabling efficient handling of multiple inputs. - **Token Authentication**: The server utilizes token authentication to secure access to the API. This tool is particularly useful for developers and researchers who want to integrate large language models into their applications or explore custom models without relying on proprietary APIs.

VITA is an open-source interactive omni multimodal Large Language Model (LLM) capable of processing video, image, text, and audio inputs simultaneously. It stands out with features like Omni Multimodal Understanding, Non-awakening Interaction, and Audio Interrupt Interaction. VITA can respond to user queries without a wake-up word, track and filter external queries in real-time, and handle various query inputs effectively. The model utilizes state tokens and a duplex scheme to enhance the multimodal interactive experience.

mcp-agent is a simple, composable framework designed to build agents using the Model Context Protocol. It handles the lifecycle of MCP server connections and implements patterns for building production-ready AI agents in a composable way. The framework also includes OpenAI's Swarm pattern for multi-agent orchestration in a model-agnostic manner, making it the simplest way to build robust agent applications. It is purpose-built for the shared protocol MCP, lightweight, and closer to an agent pattern library than a framework. mcp-agent allows developers to focus on the core business logic of their AI applications by handling mechanics such as server connections, working with LLMs, and supporting external signals like human input.

ChatPDF is a knowledge question and answer retrieval tool based on local LLM. It supports various open-source LLM models like ChatGLM3-6b, Chinese-LLaMA-Alpaca-2, Baichuan, YI, and multiple file formats including PDF, docx, markdown, txt. The tool optimizes RAG accuracy, Chinese chunk segmentation, embedding using text2vec's sentence embedding, retrieval matching with rank_BM25, and introduces reranker module for reranking candidate sets. It also enhances candidate chunk extension context, supports custom RAG models, and provides a Gradio-based RAG conversation page for seamless dialogue.

EvalPlus is a rigorous evaluation framework for LLM4Code, providing HumanEval+ and MBPP+ tests to evaluate large language models on code generation tasks. It offers precise evaluation and ranking, coding rigorousness analysis, and pre-generated code samples. Users can use EvalPlus to generate code solutions, post-process code, and evaluate code quality. The tool includes tools for code generation and test input generation using various backends.

pgvecto.rs is a Postgres extension written in Rust that provides vector similarity search functions. It offers ultra-low-latency, high-precision vector search capabilities, including sparse vector search and full-text search. With complete SQL support, async indexing, and easy data management, it simplifies data handling. The extension supports various data types like FP16/INT8, binary vectors, and Matryoshka embeddings. It ensures system performance with production-ready features, high availability, and resource efficiency. Security and permissions are managed through easy access control. The tool allows users to create tables with vector columns, insert vector data, and calculate distances between vectors using different operators. It also supports half-precision floating-point numbers for better performance and memory usage optimization.

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.

Claude Debugs For You is an MCP Server and VS Code extension that enables interactive debugging and evaluation of expressions with Claude or other LLM models. It is language-agnostic, requiring debugger console support and a valid launch.json for debugging in VSCode. Users can download the extension from releases or VS Code Marketplace, install it, and access commands through the status menu item 'Claude Debugs For You'. The tool supports debugging setups using stdio or /sse, and users can follow specific setup instructions depending on their configuration. Once set up, users can debug their code by interacting with the tool, which provides suggestions and fixes for identified issues.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.