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

该项目是一个大语言模型推理库和框架，旨在运行用于文本生成、问题解决和编码辅助的模型。它包括一个用于处理图像和文本组合输入的多模态框架，以及一个基于模型推理执行外部工具的工具调用实现。 该系统具有分布式 GPU 推理引擎，可将大型模型工作负载分散到多个图形处理器上，以提高处理速度并满足内存需求。它还通过预打包的镜像和依赖项提供容器化模型部署，以便在隔离环境中运行推理引擎。 该库涵盖了一系列功能，包括多模态输入分析、函数调用集成，以及用于预测缺失代码段的“中间填充”（fill-in-the-middle）编码。它还支持通过命令行界面进行交互式模型聊天，以维持对话会话。

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 是一个专为嵌入式运行时集成而设计的 WebAssembly 解释器。它支持在微控制器和资源受限的硬件上执行便携式二进制逻辑，并支持利用 WebAssembly 系统接口（WASI）与系统资源交互的模块。 该运行时采用基于寄存器的字节码解释和直接线程调度，将虚拟寄存器映射到物理 CPU 寄存器。它包括一个主机调用接口，用于将导入映射到 C 函数，并利用静态模块验证来确保字节码在执行前符合规范规则。 该项目通过线性内存分配限制和指令级 Gas 计量提供资源管理，以防止无限循环和系统耗尽。可观测性通过执行追踪、流监控和解释器操作分析来处理。为了开发和质量保证，该运行时包含一个交互式 REPL 并支持覆盖率引导的模糊测试。

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

该项目是使用 Lua 编程语言编写 Neovim 配置和插件的综合指南。它作为一本手册，指导如何使用原生的 Neovim Lua API 来管理缓冲区、窗口和编辑器选项。 该指南重点介绍了 Lua 与 Vimscript 之间的互操作性，提供了如何从 Lua 执行 Vimscript 以及从 Vimscript 内部调用 Lua 函数的说明。它还提供了一个插件开发框架，涵盖了将代码组织成外部模块以及创建自定义命令和键映射的内容。 该文档涵盖了更广泛的功能，包括编辑器工作流自动化、内部变量的操作以及编辑器选项的管理。它还包括有关加载外部文件和修改缓冲区行的说明。

The official PyTorch implementation of Google's Gemma models