Projects sharing features with Llama Gpt

The simplest way to run LLaMA on your local machine

NextChat is a self-hosted web application that provides a unified interface for interacting with multiple large language models. It functions as a conversational platform where users can manage and switch between diverse AI providers through configurable API backends, maintaining full control over their data and infrastructure. The platform features a persistent session layer designed to handle long-running dialogues by managing message history and context. It distinguishes itself through a structured prompt engineering environment that allows for the development and application of templates

ChatALL is a desktop application that functions as a multi-model chat client and aggregator for artificial intelligence services. It enables users to send a single prompt to multiple AI models simultaneously, allowing for the side-by-side comparison of generated responses within a unified interface. The application distinguishes itself through a local-first approach to data management, ensuring that all conversation logs and user configurations are stored directly on the user's device. This architecture supports privacy and offline access while providing a centralized system for managing and

This project is a community-curated directory of open-source software designed for deployment in private server environments and home labs. It serves as a comprehensive resource for discovering independent, self-hosted alternatives to mainstream cloud services, enabling users to maintain full data ownership and control over their digital infrastructure. The directory is structured through a hierarchical taxonomy that organizes a vast collection of applications into logical categories, ranging from media management and data analytics to private communication and team productivity tools. It dis

Llama is a computational framework and runtime environment designed for executing transformer-based neural networks locally. It functions as a generative AI inference engine, enabling the processing of input sequences through pre-trained model weights to produce text completions and structured data outputs directly on your own hardware. The system distinguishes itself through specialized memory and computation management techniques, including memory-mapped weight loading and quantization-aware inference, which allow for efficient execution on standard consumer hardware. It utilizes a stateles

vLLM is a high-throughput inference engine designed for the efficient serving and execution of large language models. It functions as a production-ready distributed model server, providing standard API protocols for online serving while also supporting offline batch processing. The system is built to maximize token generation speed and memory efficiency, enabling both large-scale cloud deployments and local execution on personal hardware. The project distinguishes itself through advanced memory management and request scheduling techniques, most notably its use of non-contiguous key-value cach

alpaca.cpp is a high-performance local inference engine implemented in C++ for executing instruction-tuned large language models. It serves as a quantized model runtime designed to load and run model tensors on local hardware with minimal dependencies, removing the requirement for a full Python environment. The project focuses on on-device text generation and the deployment of private AI chatbots. It utilizes model weight quantization to reduce memory requirements and increase inference speed on consumer-grade devices. The system covers hardware-optimized model execution through thread-pool

llama.cpp is a high-performance C++ inference engine and runtime for executing large language models locally across various hardware architectures. It provides the core components for local model execution, including a dedicated model quantizer for compressing weights into the GGUF format and a system for generating text embeddings for semantic search. The project distinguishes itself through specialized memory and execution optimizations, such as block-wise weight quantization to reduce memory footprints and memory-mapped model loading. It supports structured text generation by using formal

KoboldCPP is a local large language model inference engine and GGUF model runner designed to execute quantized models on personal hardware. It functions as a multimodal AI server and API gateway, providing OpenAI-compatible endpoints that allow third-party clients to interact with locally hosted models. The project distinguishes itself as an AI storytelling backend, featuring dedicated tools for long-form narrative management through persistent memory, world lore tracking, and character state management. It further extends its capabilities as a multimodal server capable of processing text, im

Lobe Chat is a self-hosted AI platform that provides a web-based interface for interacting with multiple large language models. It functions as an AI agent orchestrator, allowing for the design, scheduling, and management of autonomous agent teams to perform operational tasks. The platform features an extensible plugin framework and SDK to integrate external tools and custom function calls into workflows. It utilizes a provider-agnostic model layer to unify various AI APIs and includes a context-aware memory system to store structured user information for personalized interactions. The syste

WebLLM is a library for executing large language models directly within web browsers. It provides a framework for building conversational artificial intelligence applications that perform inference locally, ensuring user data privacy by eliminating the need for external server dependencies. The project distinguishes itself by leveraging browser-native graphics APIs to perform intensive machine learning computations on the client side. It maintains application responsiveness by offloading heavy model tasks to background threads and ensures continuous operation through service workers that func

Llama2.c is a minimal inference engine designed to execute transformer-based language models using only standard C code. By implementing neural network forward passes without external dependencies or complex runtime environments, it provides a lightweight execution environment for running pre-trained models. The project distinguishes itself through a focus on portability and resource efficiency. It utilizes static memory allocation to avoid dynamic heap management and maps model parameter files directly into the process address space to minimize memory overhead. The implementation relies on s

ChatGLM-6B is an open-source bilingual large language model designed for natural dialogue and text generation in both English and Chinese. It is structured as a dialogue model capable of tasks such as role-playing and information extraction. The project provides implementations for quantized language models, using low-precision weights to reduce GPU memory requirements for local inference. It also supports parameter-efficient fine-tuning, allowing model behavior to be optimized for specific tasks without requiring full retraining. The model includes capabilities for local execution on GPUs a

This project is a curated collection of deployment files and configurations for hosting a wide variety of open-source services on a home server. It primarily utilizes Docker and Docker Compose to automate the orchestration, lifecycle management, and deployment of containerized applications. The repository provides a comprehensive suite for self-hosted infrastructure, covering network management tools, media streaming, and home automation. It includes specialized configurations for securing internal services via reverse proxies, WireGuard VPN tunnels, and automated SSL/TLS certificate manageme

Olares is a comprehensive suite of self-hosted identity, storage, AI, and orchestration services designed for private infrastructure management. It functions as a Kubernetes home server orchestrator, enabling the deployment of containerized applications, AI models, and GPU resources on local hardware to replace third-party cloud services. The platform distinguishes itself through a combination of self-hosted AI infrastructure for running large language models and image generators, alongside a decentralized identity manager that uses cryptographic keys and OIDC for trustless authentication. It

Nitter is a privacy-focused, alternative web interface for viewing public social media content. It functions as a server-side proxy that fetches and renders external posts, allowing users to browse content without requiring a personal account or executing third-party tracking scripts. By stripping away user identifiers and tracking mechanisms, the application provides a lightweight, anonymous viewing experience. The project distinguishes itself through its emphasis on network-level privacy and self-sovereignty. It supports routing traffic through Tor and I2P networks to bypass censorship and

Shardeum is an autoscaling blockchain infrastructure designed to distribute network workloads across multiple shards to increase throughput. It uses a dynamic-sharding architecture that horizontally scales node capacity and adjusts the number of active shards based on real-time network demand. The system features an execution environment compatible with the Ethereum Virtual Machine, allowing it to run smart contracts and decentralized applications. It maintains network agreement and security through consensus-group partitioning, which organizes validator nodes into discrete groups. The platf

LangChain4j is a framework and library for building applications powered by large language models on the JVM. It provides a unified API for developing AI agents, implementing retrieval augmented generation, and integrating generative AI capabilities into professional software built with frameworks like Spring Boot or Quarkus. The project enables the creation of autonomous agents that can reason through tasks, manage memory, and execute external tools to achieve specific goals. It differentiates itself through a unified model interface that allows developers to switch between multiple model pr

lollms-webui is a web-based user interface and local AI model orchestrator designed for interacting with and managing large language models and multimodal AI on local hardware. It functions as a generative AI multimedia suite that enables the creation of text, images, video, and music through integrated diffusion and language models. The project features a dedicated persona manager to configure behavioral profiles and distinct personalities, controlling the style and tone of model responses. It includes a local memory system for maintaining long-term conversation context and chat history via

Project Nomad is a self-hosted survival suite and containerized offline operating environment. It provides a collection of essential tools, including a local retrieval-augmented generation system, an offline mapping server, and a local knowledge base for large language models, all designed to operate on air-gapped hardware. The system prioritizes total offline isolation to ensure telemetry-free operation. It enables private data analysis and semantic document querying through local-first vector storage and offline model execution, keeping all data on internal hardware without requiring intern

OpenLLM is a framework for deploying, managing, and scaling open-source large language models

TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics. The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads acr

Ollama provides a framework for running and managing local machine learning models. It includes a command-line interface for model lifecycle management, such as creation, embedding generation, and configuration, alongside a stable API for programmatic interaction across multiple programming languages. The platform supports the import of models and adapters in various formats, including GGUF and Safetensors. Users can define custom model behaviors, prompt templates, and system messages through a configuration file format. It also offers tools for fine-tuning models with LoRA adapters and apply

ColossalAI is a distributed deep learning framework designed for training and deploying massive artificial intelligence models across clusters of hardware accelerators. It functions as a parallel computing engine that partitions model workloads and data across multiple processors to maximize memory efficiency and throughput. The platform distinguishes itself through a comprehensive suite of parallelization strategies, including multi-dimensional tensor parallelism and pipeline-based model parallelism, which segment neural network layers and stages across devices. To support large-scale genera

This repository serves as an educational framework for building large language models from the ground up. It provides a structured curriculum that guides learners through the end-to-end lifecycle of model development, including data processing, architecture design, and optimization. By focusing on low-level implementation, the project enables users to master the fundamental mechanics of artificial intelligence without relying on high-level abstraction frameworks. The project distinguishes itself by constructing neural network components and gradient-based optimization logic from first princip

Serge is a self-hosted web chat interface for running large language models locally using the llama.cpp inference engine. It loads GGUF-format model files directly on your own machine, removing the need for internet connectivity or external API keys, and streams responses to the browser in real time via WebSocket connections. The project is packaged for containerized deployment using Docker and Docker Compose, with a Traefik reverse proxy that handles HTTP and WebSocket routing along with automatic TLS certificate management. Ready-made Kubernetes manifests are also provided, enabling deploym