Search Results for "c-support.vim"
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Showing 39 open source projects for "c-support.vim"
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1
Ollama
Run models like Kimi-K2.5, GLM-5, DeepSeek, gpt-oss, Gemma, Qwen etc.
Ollama is an open-source platform that enables developers to run large language models locally on their own machines. It simplifies working with modern AI models by providing a unified interface to download, manage, and interact with them. Users can run models like Llama, Gemma, Qwen, and others directly from the command line or through APIs. Ollama also integrates with popular developer tools and AI agents, allowing seamless workflows across coding environments and applications. It supports... -
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llama.cpp
Port of Facebook's LLaMA model in C/C++
The llama.cpp project enables the inference of Meta's LLaMA model (and other models) in pure C/C++ without requiring a Python runtime. It is designed for efficient and fast model execution, offering easy integration for applications needing LLM-based capabilities. The repository focuses on providing a highly optimized and portable implementation for running large language models directly within C/C++ environments. -
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llama.cpp
LLM inference in C/C++
llama.cpp is a high-performance C and C++ project for running large language models locally and in the cloud with minimal setup. It is built around efficient inference, broad hardware support, and the GGUF model format. The project supports many model families and has become a major foundation for local AI tools, model serving, and embedded inference workflows. It provides command-line tools, a server mode with an OpenAI-compatible API style, model conversion utilities, and extensive backend acceleration options. llama.cpp runs on CPUs and GPUs, with support for Apple silicon, x86, RISC-V, CUDA, HIP, Vulkan, SYCL, Metal, and hybrid CPU-GPU execution. ... -
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llamafile
Distribute and run LLMs with a single file
llamafile lets you distribute and run LLMs with a single file. (announcement blog post). Our goal is to make open LLMs much more accessible to both developers and end users. We're doing that by combining llama.cpp with Cosmopolitan Libc into one framework that collapses all the complexity of LLMs down to a single-file executable (called a "llamafile") that runs locally on most computers, with no installation. The easiest way to try it for yourself is to download our example llamafile for the... -
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5
GPT4All
Run Local LLMs on Any Device. Open-source
GPT4All is an open-source project that allows users to run large language models (LLMs) locally on their desktops or laptops, eliminating the need for API calls or GPUs. The software provides a simple, user-friendly application that can be downloaded and run on various platforms, including Windows, macOS, and Ubuntu, without requiring specialized hardware. It integrates with the llama.cpp implementation and supports multiple LLMs, allowing users to interact with AI models privately. This... -
6
LLPlayer
The media player for language learning, with dual subtitles
LLPlayer is an open-source media player designed specifically for language learning through video content. Unlike traditional media players, the application focuses on advanced subtitle-related features that help learners understand and interact with foreign language media more effectively. The player supports dual subtitles so users can simultaneously view text in both the original language and their native language while watching videos. It can also automatically generate subtitles in real... -
7
Emscripten
Emscripten: An LLVM-to-WebAssembly Compiler
Emscripten is a complete open-source compiler toolchain that transforms C, C++, and other LLVM-based source code into WebAssembly (and JavaScript), enabling native‑like applications to run in web browsers, Node.js, and other Wasm environments. While Emscripten mostly focuses on compiling C and C++ using Clang, it can be integrated with other LLVM-using compilers (for example, Rust has Emscripten integration, with the wasm32-unknown-emscripten and asmjs-unknown-emscripten targets). ... -
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LLamaSharp
C#/.NET binding of llama.cpp, including LLaMa/GPT model inference
The C#/.NET binding of llama.cpp. It provides APIs to infer the LLaMa Models and deploy it on the local environment. It works on both Windows, Linux and MAC without the requirement for compiling llama.cpp yourself. Its performance is close to llama.cpp. Furthermore, it provides integrations with other projects such as BotSharp to provide higher-level applications and UI. -
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RunAnywhere
Production ready toolkit to run AI locally
RunAnywhere SDKs are a set of cross-platform development tools that enable applications to run artificial intelligence models directly on user devices instead of relying on cloud infrastructure. The toolkit allows developers to integrate language models, speech recognition, and voice synthesis capabilities into mobile or desktop applications while keeping all computation local. By running models entirely on device, the platform eliminates network latency and protects user data because... -
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10
pgvecto.rs
Vector database plugin for Postgres, written in Rust
pgvecto.rs is a Postgres extension that provides vector similarity search functions. It is written in Rust and based on pgrx. It is currently under heavy development, please take care when using it in production. pgvecto.rs is a Postgres extension, which means that you can use it directly within your existing database. This makes it easy to integrate into your existing workflows and applications. pgvecto.rs supports filtering. You can set conditions when searching or retrieving points. This... -
11
TuyaOpen
Next-gen AI+IoT framework for T2/T3/T5AI/ESP32/and more
TuyaOpen is an open-source AI-enabled Internet of Things development framework designed to simplify the creation and deployment of smart connected devices. The platform provides a cross-platform C and C++ software development kit that supports a wide range of hardware platforms including Tuya microcontrollers, ESP32 boards, Raspberry Pi devices, and other embedded systems. It offers a unified development environment where developers can build devices capable of communicating with IoT cloud services while integrating AI capabilities and intelligent automation features. ... -
12
mllm
Fast Multimodal LLM on Mobile Devices
...The framework focuses on delivering high-performance AI inference in resource-constrained systems such as smartphones, embedded hardware, and lightweight computing platforms. Implemented primarily in C and C++, it is designed to operate with minimal external dependencies while taking advantage of hardware-specific acceleration technologies such as ARM NEON and x86 AVX2 instructions. The system supports multiple optimization techniques including quantization, pruning, and speculative decoding to improve performance while reducing computational overhead. ... -
13
Unity MCP
AI-powered bridge connecting LLMs and advanced AI agents
Unity-MCP is an open-source integration that connects artificial intelligence assistants with the Unity game development environment through the Model Context Protocol. The project enables AI tools such as coding assistants and autonomous agents to interact directly with Unity projects, allowing them to analyze scenes, modify assets, and generate code within the development environment. By exposing Unity editor functionality through MCP tools, the plugin allows external AI systems to... -
14
PicoLM
Run a 1-billion parameter LLM on a $10 board with 256MB RAM
...The project focuses on enabling efficient local inference by optimizing memory usage, computation, and system dependencies so that relatively large models can operate on devices with minimal RAM. It is written primarily in C and designed with a minimalist architecture that removes unnecessary dependencies and external libraries. The runtime is capable of running language models with billions of parameters on devices with only a few hundred megabytes of memory, which is significantly lower than typical LLM infrastructure requirements. This makes PicoLM particularly suitable for edge computing, offline AI applications, and embedded AI devices that cannot rely on cloud resources. -
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Semantic Kernel
Integrate cutting-edge LLM technology quickly and easily into your app
Semantic Kernel is an open-source SDK that lets you easily combine AI services like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C# and Python. By doing so, you can create AI apps that combine the best of both worlds. To help developers build their own Copilot experiences on top of AI plugins, we have released Semantic Kernel, a lightweight open-source SDK that allows you to orchestrate AI plugins. With Semantic Kernel, you can leverage the same AI orchestration patterns that power Microsoft 365 Copilot and Bing in your own apps, while still leveraging your existing development skills and investments. -
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Tencent-Hunyuan-Large
Open-source large language model family from Tencent Hunyuan
...It aims to provide competitive capability with efficient deployment and inference. FP8 quantization support to reduce memory usage (~50%) while maintaining precision. High benchmarking performance on tasks like MMLU, MATH, CMMLU, C-Eval, etc. -
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tt-metal
TT-NN operator library, and TT-Metalium low level kernel programming
tt-metal, also referred to in its documentation as TT-Metalium, is Tenstorrent’s low-level software development kit for programming applications on Tenstorrent AI accelerators. The project is designed for developers who need direct access to the company’s Tensix processor architecture, exposing a programming model that is closer to hardware control than high-level inference frameworks. Instead of following a traditional GPU model centered on massive thread parallelism, the platform is built... -
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llama2.c
Inference Llama 2 in one file of pure C
llama2.c is a minimalist implementation of the Llama 2 language model architecture designed to run entirely in pure C. Created by Andrej Karpathy, this project offers an educational and lightweight framework for performing inference on small Llama 2 models without external dependencies. It provides a full training and inference pipeline: models can be trained in PyTorch and later executed using a concise 700-line C program (run.c). -
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PowerInfer
High-speed Large Language Model Serving for Local Deployment
PowerInfer is a high-performance inference engine designed to run large language models efficiently on personal computers equipped with consumer-grade GPUs. The project focuses on improving the performance of local AI inference by optimizing how neural network computations are distributed between CPU and GPU resources. Its architecture exploits the observation that only a subset of neurons in large models are frequently activated, allowing the system to preload frequently used neurons into... -
20
Mooncake
Mooncake is the serving platform for Kimi
Mooncake is an open-source infrastructure platform designed to optimize large language model serving by focusing on efficient management and transfer of model data and KV cache. The platform was originally developed as part of the serving infrastructure for the Kimi large language model system. Its architecture centers on a high-performance transfer engine that provides unified data transfer across different storage and networking technologies. This engine enables efficient movement of... -
21
rwkv.cpp
INT4/INT5/INT8 and FP16 inference on CPU for RWKV language model
Besides the usual FP32, it supports FP16, quantized INT4, INT5 and INT8 inference. This project is focused on CPU, but cuBLAS is also supported. RWKV is a novel large language model architecture, with the largest model in the family having 14B parameters. In contrast to Transformer with O(n^2) attention, RWKV requires only state from the previous step to calculate logits. This makes RWKV very CPU-friendly on large context lengths. -
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Chat with LLMs Everywhere
Run PyTorch LLMs locally on servers, desktop and mobile
...It is intended primarily as a reference implementation that shows developers how to integrate large language models into applications without requiring a large or complex infrastructure stack. TorchChat supports running models through Python interfaces as well as integrating them directly into native applications written in languages such as C or C++. The project also demonstrates how modern LLMs like LLaMA-style models can be deployed locally while maintaining good performance across different hardware platforms. -
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Kernel Memory
Research project. A Memory solution for users, teams, and applications
Kernel Memory is an open-source reference architecture developed by Microsoft to help developers build memory systems for AI applications powered by large language models. The project focuses on enabling applications to store, index, and retrieve information so that AI systems can incorporate external knowledge when generating responses. It supports scenarios such as document ingestion, semantic search, and retrieval-augmented generation, allowing language models to answer questions using... -
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llm.c
LLM training in simple, raw C/CUDA
llm.c is a minimalist, systems-level implementation of a small transformer-based language model in C that prioritizes clarity and educational value. By stripping away heavy frameworks, it exposes the core math and memory flows of embeddings, attention, and feed-forward layers. The code illustrates how to wire forward passes, losses, and simple training or inference loops with direct control over arrays and buffers. Its compact design makes it easy to trace execution, profile hotspots, and understand the cost of each operation. ... -
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OllamaSharp
The easiest way to use Ollama in .NET
OllamaSharp is an open-source .NET library that provides strongly typed bindings for interacting with the Ollama API, making it easier for developers to integrate local large language models into C# and .NET applications. The project acts as a wrapper around the Ollama API, exposing all endpoints through asynchronous methods that allow developers to perform tasks such as generating text, creating embeddings, and managing models. It supports both local and remote Ollama instances, enabling developers to run AI models on their own hardware or connect to remote model servers. ...
