Projects sharing features with LlamaChat

This project is a web-based user interface for interacting with large language models, featuring streaming responses and persistent conversation history. It functions as an orchestration gateway that directs user prompts to specific language models and acts as a Model Context Protocol client to execute external tools and incorporate live data into conversations. The application includes a routing layer that analyzes input signals and tool requirements to dynamically direct messages to the most appropriate specialized model. It also provides customization settings for brand identity, allowing

Flowise is a low-code platform designed for building and deploying complex language model workflows through a visual, node-based interface. It functions as an orchestrator for autonomous multi-agent systems, allowing users to construct conversational pipelines by connecting language models, memory stores, and external tools on a drag-and-drop canvas. The platform distinguishes itself through its support for sophisticated agentic patterns, including supervisor-worker delegation and iterative reasoning strategies. Users can design directed acyclic graphs to manage conditional branching, state p

OptiLLM is an inference proxy and gateway router that directs prompts to specific language models based on cost, performance, and provider health. It functions as a middleware layer designed to optimize requests through intelligent routing, load balancing, and context management. The project provides specialized capabilities for data protection by anonymizing personally identifiable information before requests reach a model. It also acts as a reasoning orchestrator and tool integration layer, using inference-time loops and self-reflection to improve accuracy while connecting models to externa

Chinese-Vicuna is a Chinese large language model and instruction-following AI based on the LLaMA architecture. It is specifically designed for natural language understanding and generation in the Chinese language, utilizing an instruction-tuned model to follow complex user prompts across conversations. The project provides a LoRA fine-tuning framework and quantization systems to enable model adaptation and inference on consumer hardware. It implements quantized inference to reduce memory usage on both CPUs and GPUs, supported by a low-level C++ implementation to minimize system resource requi

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Jan is a desktop application that functions as a local artificial intelligence model runtime and an open-standard API server. It enables the execution of large language models directly on local hardware, ensuring that data remains private and accessible offline while providing a unified interface for managing model weights and inference runtimes. The platform distinguishes itself by offering a modular inference backend that allows users to swap execution engines based on hardware compatibility and performance needs. It acts as a cross-platform orchestrator, providing the ability to switch bet

A high-performance inference engine for LLM, VLM, DiT and REC models, optimized for diverse AI accelerators.

Enchanted is a privacy-focused, cross-platform chat frontend for interacting with self-hosted large language models on iOS and macOS. It serves as a native client for communicating with private model servers, specifically providing integration for the Ollama API. The application supports multimodal interactions, allowing users to combine text, image attachments, and voice prompts. It provides tools for local AI model management, including the ability to define persistent system prompts and switch between different models for specific tasks. The interface includes capabilities for rendering m

This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation library and a four-bit quantizer to reduce the GPU memory requirements needed to train large models. The framework utilizes four-bit quantization and low-rank adapters to enable model training on consumer-grade hardware. It further reduces the memory footprint through double quantization and a paged optimizer that offloads states to system RAM. The system supports distributed training across multiple GPUs to handle larger parameter scales and includes utilities for custom dataset

Open Academic Research on Improving LLaMA to SOTA LLM

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

ChatGPT爆火，开启了通往AGI的关键一步，本项目旨在汇总那些ChatGPT的开源平替们，包括文本大模型、多模态大模型等，为大家提供一些便利

Cherry Studio is a cross-platform desktop application that serves as a centralized workspace for managing and interacting with multiple artificial intelligence models. It functions as a local-first orchestrator, prioritizing user privacy by storing all conversation history and knowledge bases directly on your device. By providing a unified interface for both cloud-based and local AI services, the platform simplifies API key management and allows for consistent model interaction across different operating systems. The application distinguishes itself through a robust retrieval-augmented genera

MemGPT is a memory management framework and external memory layer for large language models. It functions as a platform for building stateful AI agents that maintain a persistent identity and continuous context across multiple sessions. The system enables agents to bypass fixed context window limitations by using a virtual context windowing approach. This allows models to manage their own memory through internal commands to search, update, and delete stored information within a hierarchical structure of short-term working context and long-term archival storage. The framework provides a local

mistral.rs is an inference engine for large language models that runs locally and exposes models behind OpenAI and Anthropic-compatible APIs. It serves as a multi-model serving platform, capable of loading several models in a single server process with per-request routing and on-demand loading and unloading. The engine supports multimodal inference, processing text alongside images, video, audio, and speech inputs, and includes a quantized model deployment runtime that reduces memory use and speeds up inference on consumer hardware. The project distinguishes itself through an agentic tool exe

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

Llama.cpp is an inference engine designed for the local execution of text-based and multimodal language models on consumer hardware. It provides a core environment for running models that process both text and image inputs, utilizing hardware-accelerated backends to optimize performance across diverse CPU and GPU architectures. The project distinguishes itself by offering a lightweight HTTP server that adheres to standard API specifications, enabling chat completion, embeddings, and reranking services. It includes a suite of tools for model quantization and conversion, which reduces memory us

Parallax is a distributed model serving framework that lets you build your own AI cluster anywhere

Infinity is a distributed vector database and multimodal vector store designed to manage large-scale datasets for retrieval and similarity search. It serves as a backend for large language model applications and retrieval augmented generation pipelines by storing and retrieving dense vectors, sparse vectors, and full-text data. The system functions as a hybrid search engine, combining vector embeddings and full-text search with reranking algorithms to identify the most relevant documents. It supports multimodal data storage, allowing the maintenance of diverse data types including tensors, st

lmdeploy is a high-performance inference engine and deployment framework for large language models and vision models. It functions as a multi-modal model server and compression toolkit designed to serve models with high throughput and low latency. The system enables the distribution of model services across multiple machines using request-based load balancing and tensor parallelism. It includes specialized tools for model quantization and compression to reduce the memory footprint of weights and caches. The framework covers broad capability areas including production deployment, distributed

Shepherd: A foundational framework enabling federated instruction tuning for large language models