Search Results for "llama.cpp"
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16 projects for "llama.cpp" with 2 filters applied:
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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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wllama
WebAssembly binding for llama.cpp - Enabling on-browser LLM inference
wllama is a WebAssembly-based library that enables large language model inference directly inside a web browser. Built as a binding for the llama.cpp inference engine, the project allows developers to run LLM models locally without requiring a server backend or dedicated GPU hardware. The library leverages WebAssembly SIMD capabilities to achieve efficient execution within modern browsers while maintaining compatibility across platforms. By running models locally on the user’s device, wllama enables privacy-preserving AI applications that do not require sending data to remote servers. ... -
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GPT4All
Run Local LLMs on Any Device. Open-source
...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 project also supports Python integrations for easy automation and customization. GPT4All is ideal for individuals and businesses seeking private, offline access to powerful LLMs. -
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Clippy
Clippy, now with some AI
...The project serves as both a playful homage to the early days of personal computing and a practical demonstration of local AI inference. Clippy integrates with the llama.cpp runtime to run models directly on a user’s computer without requiring cloud-based AI services. It supports models in the GGUF format, which allows it to run many publicly available open-source LLMs efficiently on consumer hardware. Users interact with the system through a simple animated assistant interface that can answer questions, generate text, and perform conversational tasks. ... -
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OuteTTS
Interface for OuteTTS models
...It provides a high-level Interface API that wraps model configuration, speaker handling, and audio generation so you can focus on integrating speech into your application rather than wiring up low-level engines. The project supports multiple backends including llama.cpp (Python bindings and server), Hugging Face Transformers, ExLlamaV2, VLLM and a JavaScript interface via Transformers.js, allowing it to run on CPUs, NVIDIA CUDA GPUs, AMD ROCm, Vulkan-capable GPUs, and Apple Metal. It also includes a notion of speaker profiles: you can create a speaker from a short audio sample, save it as JSON, and reuse it for consistent voice identity across generations and sessions. ... -
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llama.vscode
VS Code extension for LLM-assisted code/text completion
llama.vscode is a Visual Studio Code extension that provides AI-assisted coding features powered primarily by locally running language models. The extension is designed to be lightweight and efficient, enabling developers to use AI tools even on consumer-grade hardware. It integrates with the llama.cpp runtime to run language models locally, eliminating the need to rely entirely on external APIs or cloud providers. The extension supports common AI development features such as code completion, conversational chat assistance, and AI-assisted code editing directly within the IDE. Developers can select and manage models through a configuration interface that automatically downloads and runs the required models locally. ... -
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Harbor LLM
Run a full local LLM stack with one command using Docker
...With a single command, users can start preconfigured tools like Ollama and Open WebUI, enabling chat, workflows, and integrations immediately. Harbor supports multiple inference engines, including llama.cpp and vLLM, and connects them seamlessly to user interfaces. It also includes tools for web retrieval, image generation, voice interaction, and workflow automation. Built on Docker, Harbor allows services to run in isolated containers while communicating over a local network. It is intended for local development and experimentation rather than production deployment, giving developers a flexible way to explore AI systems, test configurations, and manage complex LLM stacks without manual wiring or setup overhead. -
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OpenMonoAgent
Terminal-native coding agent powered by local LLMs
OpenMonoAgent.ai is a self-hosted coding agent designed to run entirely on the user’s own hardware. It pairs a .NET CLI with a local llama.cpp inference server so developers can use agentic coding workflows without cloud subscriptions or per-token billing. The project emphasizes privacy, local control, and ownership of the model, compute, and project data. It includes a terminal-native workflow, built-in tools, Docker sandboxing, and code intelligence features. The system can run on CPU or GPU and is designed to auto-configure itself when possible. ... -
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Paddler
Open-source LLM load balancer and serving platform for hosting LLMs
...The system acts as a specialized load balancer and serving layer for language models, enabling organizations to run inference workloads without relying on external API providers. It supports running models locally through engines such as llama.cpp while distributing requests across multiple compute nodes to improve performance and reliability. The architecture is designed with privacy and cost control in mind, making it suitable for organizations that handle sensitive data or require predictable operational costs. Paddler also includes tools for monitoring, request buffering, and autoscaling integration so that deployments can adapt dynamically to changing workloads. ... -
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Outlines
Structured Outputs
...Developers can require predefined choices, primitive Python types, Pydantic models, JSON schemas, regular expressions, function signatures, or custom grammars. The same interface works with local models, inference servers, and hosted APIs from several providers. Supported integrations include Transformers, llama.cpp, vLLM, Ollama, OpenAI, and Gemini. It also provides reusable prompt templates and application abstractions for packaging prompts with output constraints. Outlines is designed for extraction, classification, tool calling, automation, and other production workflows that require valid machine-readable results. -
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Hy-MT2 is a family of fast-thinking multilingual translation models built for complex real-world translation scenarios. It includes 1.8B, 7B, and 30B-A3B model sizes, giving users options for lightweight deployment, stronger general performance, or MoE-based capacity. The models support translation across 33 languages and are designed to follow detailed translation instructions in multiple languages. They can handle tasks involving terminology, style, personalization, delimiters, structured...
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Orpheus TTS
Towards Human-Sounding Speech
Orpheus TTS is a state-of-the-art open-source text-to-speech system built on a Llama-3B backbone, treating speech synthesis as a large language model problem instead of a traditional TTS pipeline. It is designed to produce human-like speech with natural intonation, emotion, and rhythm, targeting quality comparable to or better than many closed-source systems. The project ships both pretrained and finetuned English models, as well as a family of multilingual models released as a research... -
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LlamaGPT
Self-hosted ChatGPT-like chatbot powered by Llama models locally
...It supports models such as Llama 2 and Code Llama, allowing users to perform both general conversation and programming-related tasks. It integrates components built around the llama.cpp ecosystem to efficiently run models on consumer hardware. It can be deployed using containerized setups and supports environments ranging from personal computers to self-hosted servers. -
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SuperGemma4
Fast uncensored Gemma model optimized for local chat and coding
...Unlike raw base models, it inherits improvements from the SuperGemma Fast line, resulting in better performance in logic, coding, and real-world text workflows. The model is packaged in GGUF format for efficient use with llama.cpp and has been specifically tested on Apple Silicon hardware, delivering high token speeds and smooth local inference. A neutral chat template is embedded to prevent prompt misrouting issues, ensuring consistent responses without unintended shifts into coding or tool-use modes. -
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Mellum-4b-base
JetBrains’ 4B parameter code model for completions
...With a context window of 8,192 tokens, it excels at code completion, fill-in-the-middle tasks, and intelligent code suggestions for professional developer tools and IDEs. The model is efficient for both cloud inference with vLLM and local deployment using llama.cpp or Ollama, thanks to its bf16 precision and AMP training. While the base model is not fine-tuned for downstream tasks, it is designed to be easily adapted through supervised fine-tuning (SFT) or reinforcement learning (RL). Benchmarks on RepoBench, SAFIM, and HumanEval demonstrate its competitive performance, with specialized fine-tuned versions for Python already showing strong improvements. -
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Jan-v1-edge
Jan-v1-edge: efficient 1.7B reasoning model optimized for edge devices
Jan-v1-edge is a lightweight agentic language model developed by JanHQ, designed for fast and reliable on-device execution. It is the second release in the Jan Family and was distilled from the larger Jan-v1 model, retaining strong reasoning and problem-solving capabilities while reducing its computational footprint. The model was refined through a two-stage post-training process: Supervised Fine-Tuning (SFT) to transfer knowledge from Jan-v1, followed by Reinforcement Learning with...
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