Search Results for "integration"
Sort By:
Showing 28 open source projects for "integration"
View related business solutions-
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
1
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. -
2
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... -
3
vLLM
A high-throughput and memory-efficient inference and serving engine
vLLM is a fast and easy-to-use library for LLM inference and serving. High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more. -
4
Chipper
AI interface for tinkerers (Ollama, Haystack RAG, Python)
Chipper is an AI interface designed for tinkerers and developers, providing a platform to experiment with various AI models and techniques. It offers integration with tools like Ollama and Haystack for Retrieval-Augmented Generation (RAG), enabling users to build and test AI applications efficiently. Chipper supports Python and provides a modular architecture, allowing for customization and extension based on specific project requirements. -
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
-
5
optillm
Optimizing inference proxy for LLMs
OptiLLM is an optimizing inference proxy for Large Language Models (LLMs) that implements state-of-the-art techniques to enhance performance and efficiency. It serves as an OpenAI API-compatible proxy, allowing for seamless integration into existing workflows while optimizing inference processes. OptiLLM aims to reduce latency and resource consumption during LLM inference. -
6
Open WebUI
User-friendly AI Interface
...It supports various LLM runners like Ollama and OpenAI-compatible APIs, with a built-in inference engine for Retrieval Augmented Generation (RAG), making it a powerful AI deployment solution. Key features include effortless setup via Docker or Kubernetes, seamless integration with OpenAI-compatible APIs, granular permissions and user groups for enhanced security, responsive design across devices, and full Markdown and LaTeX support for enriched interactions. Additionally, Open WebUI offers a Progressive Web App (PWA) for mobile devices, providing offline access and a native app-like experience. The platform also includes a Model Builder, allowing users to create custom models from base Ollama models directly within the interface. ... -
7
LocalAI
The free, Open Source alternative to OpenAI, Claude and others
LocalAI is an open-source platform that allows users to run large language models and other AI systems locally on their own hardware. It acts as a drop-in replacement for APIs such as OpenAI, enabling developers to build AI-powered applications without relying on external cloud services. The platform supports a wide range of model types, including text generation, image creation, speech processing, and embeddings. LocalAI can run on consumer-grade hardware and does not necessarily require a... -
8
Lean Copilot
LLMs as Copilots for Theorem Proving in Lean
LeanCopilot integrates large language models (LLMs) as copilots for theorem proving in the Lean proof assistant. It assists users by suggesting tactics, premises, and searching for proofs, thereby enhancing the efficiency of formal verification processes. LeanCopilot supports both built-in models from LeanDojo and custom models, offering flexibility for various use cases. -
9
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. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
10
Embedding Studio
Framework which allows you transform your Vector Database
Embedding Studio is a framework that transforms vector databases into feature-rich search engines. It leverages embeddings to enhance search capabilities, enabling more accurate and context-aware retrieval of information. Embedding Studio supports various data types and integrates seamlessly with existing databases, providing tools for fine-tuning and optimizing embeddings to suit specific application needs. -
11
LLM.swift
LLM.swift is a simple and readable library
LLM.swift is a Swift package that enables developers to run Large Language Models (LLMs) directly on Apple devices, including iOS, macOS, and watchOS. By leveraging Apple's hardware and software optimizations, LLM.swift facilitates on-device natural language processing tasks, ensuring user privacy and reducing latency associated with cloud-based solutions. -
12
Artificial Intelligence Controller
AICI: Prompts as (Wasm) Programs
AICI is a framework that allows developers to build controllers that constrain and direct the output of Large Language Models (LLMs). By treating prompts as WebAssembly (Wasm) programs, AICI enables more precise and controlled interactions with LLMs, enhancing their utility in various applications. This approach allows for the creation of more reliable and predictable AI-driven systems. -
13
LMDeploy
LMDeploy is a toolkit for compressing, deploying, and serving LLMs
LMDeploy is a toolkit designed for compressing, deploying, and serving large language models (LLMs). It offers tools and workflows to optimize LLMs for production environments, ensuring efficient performance and scalability. LMDeploy supports various model architectures and provides deployment solutions across different platforms. -
14
Text Generation Inference
Large Language Model Text Generation Inference
Text Generation Inference is a high-performance inference server for text generation models, optimized for Hugging Face's Transformers. It is designed to serve large language models efficiently with optimizations for performance and scalability. -
15
Intel Extension for Transformers
Build your chatbot within minutes on your favorite device
Intel Extension for Transformers is an innovative toolkit designed to accelerate Transformer-based models on Intel platforms, including CPUs and GPUs. It offers state-of-the-art compression techniques for Large Language Models (LLMs) and provides tools to build chatbots within minutes on various devices. The extension aims to optimize the performance of Transformer-based models, making them more efficient and accessible. -
16
LazyLLM
Easiest and laziest way for building multi-agent LLMs applications
LazyLLM is an optimized, lightweight LLM server designed for easy and fast deployment of large language models. It is fully compatible with the OpenAI API specification, enabling developers to integrate their own models into applications that normally rely on OpenAI’s endpoints. LazyLLM emphasizes low resource usage and fast inference while supporting multiple models. -
17
gemma.cpp
lightweight, standalone C++ inference engine for Google's Gemma models
Gemma.cpp is a C++ implementation for running inference with Gemma models efficiently on CPUs and GPUs. Developed by Google, it allows running large language models (LLMs) like Gemma with minimal hardware, focusing on optimized performance and low latency. Gemma.cpp is intended for developers seeking to deploy LLMs in production environments without needing massive computational resources. -
18
LitGPT
20+ high-performance LLMs with recipes to pretrain, finetune at scale
LitGPT is a collection of over 20 high-performance large language models (LLMs) accompanied by recipes to pretrain, finetune, and deploy them at scale. It provides implementations without abstractions, making it beginner-friendly while offering advanced features like flash attention and support for various precision levels. LitGPT is designed to run efficiently across multiple GPUs or TPUs, catering to both small-scale and large-scale deployments. -
19
TalkingHeads
A library to communicate with ChatGPT, Claude, Copilot, Gemini
TalkingHeads is a Python library designed to facilitate communication with various AI chat agents, including ChatGPT, Claude, Copilot, Gemini, HuggingChat, and Pi. It provides a unified interface for interacting with these platforms, simplifying the integration of conversational AI capabilities into applications. TalkingHeads supports browser automation and offers tools to manage sessions, handle prompts, and process responses effectively. -
20
BrowserAI
Run local LLMs like llama, deepseek, kokoro etc. inside your browser
BrowserAI is a cutting-edge platform that allows users to run large language models (LLMs) directly in their web browser without the need for a server. It leverages WebGPU for accelerated performance and supports offline functionality, making it a highly efficient and privacy-conscious solution. The platform provides a developer-friendly SDK with pre-configured popular models, and it allows for seamless switching between MLC and Transformer engines. Additionally, it supports features such as... -
21
RamaLama
Simplifies the local serving of AI models from any source
...RamaLama supports multiple model registries and offers a REST API or chatbot interface for interacting with running models, making it flexible for local development, testing, or integration into larger systems. -
22
PaddleSpeech
Easy-to-use Speech Toolkit including Self-Supervised Learning model
PaddleSpeech is an open-source toolkit on PaddlePaddle platform for a variety of critical tasks in speech and audio, with state-of-art and influential models. Via the easy-to-use, efficient, flexible and scalable implementation, our vision is to empower both industrial application and academic research, including training, inference & testing modules, and deployment process. Low barriers to install, CLI, Server, and Streaming Server is available to quick-start your journey. We provide... -
23
Seldon Core
An MLOps framework to package, deploy, monitor and manage models
...Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and deployment (CI/CD) tools to scale and update your deployment. Built on Kubernetes, runs on any cloud and on-premises. Framework agnostic, supports top ML libraries, toolkits and languages. Advanced deployments with experiments, ensembles and transformers. Our open-source framework makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. ... -
24
TensorFlow Serving
Serving system for machine learning models
...It deals with the inference aspect of machine learning, taking models after training and managing their lifetimes, providing clients with versioned access via a high-performance, reference-counted lookup table. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data. The easiest and most straight-forward way of using TensorFlow Serving is with Docker images. We highly recommend this route unless you have specific needs that are not addressed by running in a container. In order to serve a Tensorflow model, simply export a SavedModel from your Tensorflow program. ... -
25
AudioMuse-AI is an open-source, Dockerized environment that brings automatic playlist generation to your self-hosted music library. Using tools such as Librosa and ONNX, it performs sonic analysis on your audio files locally, allowing you to curate playlists for any mood or occasion without relying on external APIs. Deploy it easily on your local machine with Docker Compose or Podman, or scale it in a Kubernetes cluster (supports AMD64 and ARM64). It integrates with the main music...
