Awesome GitHub RepositoriesFunction Calling Interfaces

Qwen-7B is a pretrained causal language model designed for natural language generation, text processing, and complex reasoning tasks. It is available as an instruction-tuned model optimized for conversational interactions and a tool-use model capable of executing function calls and interacting with external APIs. The project provides a quantized version of the model to reduce GPU memory usage and supports the development of autonomous agents that can execute code and perform functions to complete complex goals. The system covers a wide range of capabilities including model fine-tuning throug

CodeQwen1.5 is a large language model designed for generating, completing, and analyzing code. It functions as an AI code generator capable of writing programming logic across hundreds of different languages. The model is distinguished by its long-context capabilities, allowing it to process up to one million tokens to reason across entire software repositories. It also operates as a function calling model, utilizing specialized formats to execute complex coding tasks and browser-based automation. The system supports intelligent code completion through fill-in-the-middle capabilities, which

Este proyecto es una biblioteca y framework de inferencia de modelos de lenguaje de gran tamaño (LLM) diseñado para ejecutar modelos para generación de texto, resolución de problemas y asistencia en codificación. Incluye un framework multimodal para procesar entradas combinadas de imagen y texto, y una implementación de uso de herramientas que permite la ejecución de funciones externas basadas en el razonamiento del modelo. El sistema cuenta con un motor de inferencia de GPU distribuido que reparte las cargas de trabajo de modelos grandes a través de múltiples procesadores gráficos para aumentar la velocidad de procesamiento y cumplir con los requisitos de memoria. También proporciona despliegue de modelos en contenedores a través de imágenes preempaquetadas y dependencias para servir motores de inferencia en entornos aislados. La biblioteca cubre una gama de capacidades que incluyen análisis de entrada multimodal, integración de llamadas a funciones y codificación de relleno (fill-in-the-middle) para predecir segmentos de código faltantes. Además, admite chat interactivo con el modelo a través de una interfaz de línea de comandos para mantener sesiones conversacionales.

Mistral Inference is a library for running Mistral large language models on a GPU, generating text from prompts with token streaming. It loads pretrained model weights from local disk or a remote registry into GPU memory, then produces output tokens one by one for real-time display in interactive applications. The library supports multimodal prompts that accept image URLs alongside text, enabling visual description and reasoning. It includes content safety guardrails that scan generated text against predefined policies to block or flag policy violations. For structured interactions, it provid

WasmEdge is an extensible WebAssembly runtime that executes WebAssembly bytecode in a secure sandbox for cloud, edge, and embedded applications. It functions as a multi-language compiler, compiling applications written in Rust, JavaScript, Go, and Python into WebAssembly bytecode for sandboxed execution, and as a server-side JavaScript runtime that runs JavaScript programs with ES6 modules, NPM packages, and Node.js-compatible APIs. The runtime also serves as an AI inference runtime, executing AI models from JavaScript using WASI-NN plug-ins for inference tasks on personal devices and edge har

This project is a terminal-based command line interface client and agent orchestrator for interacting with multiple large language model providers. It functions as an OpenAI API client and a local API gateway that exposes chat completions and embeddings through an HTTP server. The system distinguishes itself by providing a retrieval-augmented generation tool for indexing local files and URLs into a vector database to provide custom document context. It allows for the creation of specialized AI agents that combine custom system prompts with tool calling and external function execution. The to

Spring AI is an application framework for Java that provides a portable, fluent API for integrating AI models, tools, and vector stores into applications. It wraps multiple AI providers behind a common interface, allowing developers to switch between chat, embedding, image, and speech models without changing application code. The framework includes a chainable chat client API similar to WebClient or RestClient, supports both synchronous and streaming interactions, and offers structured output conversion that transforms unstructured AI responses into strongly-typed Java objects. The framework

This project is a Java-based framework integration that provides an AI agent runtime, a graph-based AI workflow engine, and an LLM orchestration framework for Spring applications. It enables the development of stateful autonomous agents and the implementation of retrieval-augmented generation systems using document processing and vector databases. The framework distinguishes itself through a graph-based workflow runtime for designing complex AI pipelines with conditional routing and persistent state. It supports multi-agent orchestration via service-discovery coordination and provides human-i

Wasm3 es un intérprete de WebAssembly diseñado para la integración en tiempo de ejecución embebido. Permite la ejecución de lógica binaria portátil en microcontroladores y hardware con recursos limitados, proporcionando soporte para módulos que utilizan la WebAssembly System Interface (WASI) para interactuar con los recursos del sistema. El entorno de ejecución emplea interpretación de bytecode basada en registros y despacho de hilos directos para mapear registros virtuales a registros físicos de la CPU. Incluye una interfaz de llamadas al host para mapear importaciones a funciones de C y utiliza validación estática de módulos para garantizar que el bytecode siga las reglas de la especificación antes de la ejecución. El proyecto proporciona gestión de recursos mediante límites de asignación de memoria lineal y medición de gas a nivel de instrucción para evitar bucles infinitos y el agotamiento del sistema. La observabilidad se maneja mediante rastreo de ejecución, monitoreo de flujo y perfilado de operaciones del intérprete. Para el desarrollo y control de calidad, el entorno de ejecución incluye un REPL interactivo y admite fuzzing guiado por cobertura.

ERNIE is a development toolkit for training, fine-tuning, and deploying large language models built on the PaddlePaddle deep learning platform. It provides a comprehensive suite of core components, including an inference server for vision and language models, a training and fine-tuning toolkit, and a framework for building retrieval-augmented generation systems using private knowledge bases. The project features multimodal AI models capable of reasoning across text, images, and video to perform complex visual understanding and information extraction. It distinguishes itself through specialize

GLM-4 is an open weights large language model designed as a multimodal chat system. It functions as a reasoning-focused and multilingual model capable of processing and generating responses across text and visual data types. The model is distinguished by its function-calling capabilities, allowing it to interface with external tools and APIs to execute tasks and retrieve real-time information. It is optimized for complex logical reasoning, mathematical problem solving, and deep research involving long-form content generation. Broad capabilities include multilingual text generation, the creat

GLM-4 is a large language model and fine-tuning framework designed for human-like text production, complex reasoning, and multilingual conversation. It functions as a multimodal system capable of processing high-resolution visual content and as a long-context model designed to analyze documents with a context window of up to one million tokens. The project differentiates itself through a function calling interface that enables AI agent development by connecting the model to external APIs and real-time web browsing. It includes specialized capabilities for generating functional programming cod

Guardrails is a Python SDK that wraps calls to large language models with configurable validation pipelines, corrective actions, and structured output generation. It provides a unified API layer that connects to over 100 language models, applying consistent validation, streaming, and error-handling across providers. The framework validates and corrects model responses against safety and quality rules, detecting and mitigating risks in both inputs and outputs using pre-built and custom validators. The project distinguishes itself through a validator-pipeline architecture that sequentially appl

Gopher-lua is a complete implementation of the Lua language and its standard libraries written natively in Go. It serves as an embedded scripting engine and virtual machine that allows Go applications to execute Lua scripts and exchange data between the host and the script environment. The project provides a bytecode compiler to transform source code into a binary format for faster execution. It enables deep integration between the two languages by allowing the registration of native Go functions to be called from scripts, and the invocation of script functions directly from Go. The engine c

llm-zoomcamp is a comprehensive educational program and course for building real-life AI systems using large language models. It serves as a structured curriculum and implementation guide for developing AI applications and retrieval techniques. The project provides instructional material on building retrieval augmented generation pipelines to ground model responses in custom knowledge bases. It includes training on vector database implementation, semantic search, and the use of function calling to create autonomous agentic workflows. The curriculum covers a broad range of system development

Detours is a library for intercepting Win32 API calls and redirecting function calls at runtime on Windows, enabling binary-level instrumentation without requiring access to the original source code. It functions as an API hooking library and binary instrumentation toolkit, allowing developers to monitor or modify the behavior of compiled Windows binaries by hooking into their function execution paths. The project achieves this through detour-based function interception, where the first few instructions of a target function are replaced with a jump to a user-supplied detour function, while pr

Este proyecto es una guía completa para escribir configuraciones y plugins de Neovim utilizando el lenguaje de programación Lua. Sirve como manual para usar la API nativa de Lua en Neovim para gestionar buffers, ventanas y opciones del editor. La guía se centra en la interoperabilidad entre Lua y Vimscript, proporcionando instrucciones sobre cómo ejecutar Vimscript desde Lua y llamar a funciones de Lua desde Vimscript. También proporciona un framework para el desarrollo de plugins, cubriendo la organización del código en módulos externos y la creación de comandos y mapeos de teclas personalizados. La documentación cubre capacidades más amplias, incluyendo la automatización del flujo de trabajo del editor, la manipulación de variables internas y la gestión de opciones del editor. También incluye instrucciones para cargar archivos externos y modificar líneas de buffer.

The official PyTorch implementation of Google's Gemma models