p1

p1 is a code completion engine based on Large Language Models (LLM) that operates at the edge. It provides intelligent code suggestions and completions to enhance the coding experience. The tool is designed to assist developers in writing code more efficiently by predicting and offering context-aware completions based on the code being written. With implementations available for popular code editors like Vim and Visual Studio Code, p1 aims to improve productivity and streamline the coding process for software developers.

This repository contains a collection of tools and scripts for generating code at the repository level. It provides a set of utilities to automate the process of creating and managing code across multiple files and directories. The tools included in this repository aim to improve code generation efficiency and maintainability by streamlining the development workflow. With a focus on enhancing productivity and reducing manual effort, this collection offers a variety of code generation options and customization features to suit different project requirements.

KTransformers is a flexible Python-centric framework designed to enhance the user's experience with advanced kernel optimizations and placement/parallelism strategies for Transformers. It provides a Transformers-compatible interface, RESTful APIs compliant with OpenAI and Ollama, and a simplified ChatGPT-like web UI. The framework aims to serve as a platform for experimenting with innovative LLM inference optimizations, focusing on local deployments constrained by limited resources and supporting heterogeneous computing opportunities like GPU/CPU offloading of quantized models.

Spec Workflow MCP is a Model Context Protocol (MCP) server that offers structured spec-driven development workflow tools for AI-assisted software development. It includes a real-time web dashboard and a VSCode extension for monitoring and managing project progress directly in the development environment. The tool supports sequential spec creation, real-time monitoring of specs and tasks, document management, archive system, task progress tracking, approval workflow, bug reporting, template system, and works on Windows, macOS, and Linux.

Enterprise h2oGPTe - GenAI RAG is a repository containing code examples, notebooks, and benchmarks for the enterprise version of h2oGPTe, a powerful AI tool for generating text based on the RAG (Retrieval-Augmented Generation) architecture. The repository provides resources for leveraging h2oGPTe in enterprise settings, including implementation guides, performance evaluations, and best practices. Users can explore various applications of h2oGPTe in natural language processing tasks, such as text generation, content creation, and conversational AI.

Fast-DLLM is a diffusion-based Large Language Model (LLM) inference acceleration framework that supports efficient inference for models like Dream and LLaDA. It offers fast inference support, multiple optimization strategies, code generation, evaluation capabilities, and an interactive chat interface. Key features include Key-Value Cache for Block-Wise Decoding, Confidence-Aware Parallel Decoding, and overall performance improvements. The project structure includes directories for Dream and LLaDA model-related code, with installation and usage instructions provided for using the LLaDA and Dream models.

RAFT (Reusable Accelerated Functions and Tools) is a C++ header-only template library with an optional shared library that contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

Bisheng is a leading open-source **large model application development platform** that empowers and accelerates the development and deployment of large model applications, helping users enter the next generation of application development with the best possible experience.

OpenSPG is a knowledge graph engine developed by Ant Group in collaboration with OpenKG, based on the SPG (Semantic-enhanced Programmable Graph) framework. It provides explicit semantic representations, logical rule definitions, operator frameworks (construction, inference), and other capabilities for domain knowledge graphs. OpenSPG supports pluggable adaptation of basic engines and algorithmic services by various vendors to build customized solutions.

Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility. For more architecture details, see Milvus Architecture Overview. Milvus was released under the open-source Apache License 2.0 in October 2019. It is currently a graduate project under LF AI & Data Foundation.

This project provides Rust code that follows the text 'Build An LLM From Scratch' by Sebastian Raschka. It translates PyTorch code into Rust using the Candle crate, aiming to build a GPT-style LLM. Users can clone the repo, run examples/exercises, and access the same datasets as in the book. The project includes chapters on understanding large language models, working with text data, coding attention mechanisms, implementing a GPT model, pretraining unlabeled data, fine-tuning for classification, and fine-tuning to follow instructions.

The 'airflow-provider-great-expectations' repository contains a set of Airflow operators for Great Expectations, a Python library used for testing and validating data. The operators enable users to run Great Expectations validations and checks within Apache Airflow workflows. The package requires Airflow 2.1.0+ and Great Expectations >=v0.13.9. It provides functionalities to work with Great Expectations V3 Batch Request API, Checkpoints, and allows passing kwargs to Checkpoints at runtime. The repository includes modules for a base operator and examples of DAGs with sample tasks demonstrating the operator's functionality.

Compute Blade is a feature-rich carrier board designed for the Raspberry Pi Compute Module 4 and compatible alternatives, aimed at simplifying and automating the development and management of on-premise cluster environments. The solution streamlines complex processes, making cluster setup and management efficient and manageable for users, from homelabs to advanced AI clusters at scale.

The OpenVINO Build and Deploy repository provides pre-built components and code samples to accelerate the development and deployment of production-grade AI applications across various industries. With the OpenVINO Toolkit from Intel, users can enhance the capabilities of both Intel and non-Intel hardware to meet specific needs. The repository includes AI reference kits, interactive demos, workshops, and step-by-step instructions for building AI applications. Additional resources such as Jupyter notebooks and a Medium blog are also available. The repository is maintained by the AI Evangelist team at Intel, who provide guidance on real-world use cases for the OpenVINO toolkit.

MS-Agent is a lightweight framework designed to empower agents with autonomous exploration capabilities. It provides a flexible and extensible architecture for creating agents capable of tasks like code generation, data analysis, and tool calling with MCP support. The framework supports multi-agent interactions, deep research, code generation, and is lightweight and extensible for various applications.

SinkFinder + LLM is a closed-source semi-automatic vulnerability discovery tool that performs static code analysis on jar/war/zip files. It enhances the capability of LLM large models to verify path reachability and assess the trustworthiness score of the path based on the contextual code environment. Users can customize class and jar exclusions, depth of recursive search, and other parameters through command-line arguments. The tool generates rule.json configuration file after each run and requires configuration of the DASHSCOPE_API_KEY for LLM capabilities. The tool provides detailed logs on high-risk paths, LLM results, and other findings. Rules.json file contains sink rules for various vulnerability types with severity levels and corresponding sink methods.

Zsh Codex is a ZSH plugin that enables AI-powered code completion in the command line. It supports both OpenAI's Codex and Google's Generative AI (Gemini), providing advanced language model capabilities for coding tasks directly in the terminal. Users can easily install the plugin and configure it to enhance their coding experience with AI assistance.

wtf.nvim is a Neovim plugin that enhances diagnostic debugging by providing explanations and solutions for code issues using ChatGPT. It allows users to search the web for answers directly from Neovim, making the debugging process faster and more efficient. The plugin works with any language that has LSP support in Neovim, offering AI-powered diagnostic assistance and seamless integration with various resources for resolving coding problems.

PearAI Submodule / Extension is the source code for the bulk of PearAI's functionality, bundled as a VSCode / PearAI extension. It allows users to easily understand code sections, refactor functions, and ask questions by mentioning a file. The tool aims to enhance coding experience and productivity within the VSCode environment.

zsh-github-copilot is a `zsh` plugin that enhances the GitHub Copilot experience by providing keybinds to quickly access command explanations and get Copilot suggestions. It integrates seamlessly with GitHub CLI and offers a smooth setup process. Users can easily install the plugin using popular zsh plugin managers like antigen, oh-my-zsh, zinit, zplug, and zpm. By binding specific keys, users can access the 'suggest' and 'explain' functionalities to improve their coding workflow with GitHub Copilot. This plugin is designed to streamline the usage of GitHub Copilot within the zsh shell environment.

p1 is a code completion engine based on Large Language Models (LLM) that operates at the edge. It provides intelligent code suggestions and completions to enhance the coding experience. The tool is designed to assist developers in writing code more efficiently by predicting and offering context-aware completions based on the code being written. With implementations available for popular code editors like Vim and Visual Studio Code, p1 aims to improve productivity and streamline the coding process for software developers.

Llama Coder is a self-hosted Github Copilot replacement for VS Code that provides autocomplete using Ollama and Codellama. It works best with Mac M1/M2/M3 or RTX 4090, offering features like fast performance, no telemetry or tracking, and compatibility with any coding language. Users can install Ollama locally or on a dedicated machine for remote usage. The tool supports different models like stable-code and codellama with varying RAM/VRAM requirements, allowing users to optimize performance based on their hardware. Troubleshooting tips and a changelog are also provided for user convenience.

The Mattermost AI Copilot Plugin is an extension that adds functionality for local and third-party LLMs within Mattermost v9.6 and above. It is currently experimental and allows users to interact with AI models seamlessly. The plugin enhances the user experience by providing AI-powered assistance and features for communication and collaboration within the Mattermost platform.

PearAI is an AI-powered code editor designed to enhance development by reducing the amount of coding required. It is a fork of VSCode and the main functionality lies within the 'extension/pearai' submodule. Users can contribute to the project by fixing issues, submitting bugs and feature requests, reviewing source code changes, and improving documentation. The tool aims to streamline the coding process and provide an efficient environment for developers to work in.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.