Best AI tools for< Serve Llms >

Substratus.AI is a fully managed private LLMs platform that allows users to serve LLMs (Llama and Mistral) in their own cloud account. It enables users to keep control of their data while reducing OpenAI costs by up to 10x. With Substratus.AI, users can utilize LLMs in production in hours instead of weeks, making it a convenient and efficient solution for AI model deployment.

Predibase is a platform for fine-tuning and serving Large Language Models (LLMs). It provides a cost-effective and efficient way to train and deploy LLMs for a variety of tasks, including classification, information extraction, customer sentiment analysis, customer support, code generation, and named entity recognition. Predibase is built on proven open-source technology, including LoRAX, Ludwig, and Horovod.

Empower is a serverless fine-tuned LLM hosting platform that offers a developer platform for fine-tuned LLMs. It provides prebuilt task-specific base models with GPT4 level response quality, enabling users to save up to 80% on LLM bills with just 5 lines of code change. Empower allows users to own their models, offers cost-effective serving with no compromise on performance, and charges on a per-token basis. The platform is designed to be user-friendly, efficient, and cost-effective for deploying and serving fine-tuned LLMs.

LanguageGUI is an open-source design system and UI Kit for giving LLMs the flexibility of formatting text outputs into richer graphical user interfaces. It includes dozens of unique UI elements that serve different use cases for rich conversational user interfaces, such as 100+ UI components & customizable screens, 10+ conversational UI widgets, 20+ chat bubbles, 30+ pre-built screens to kickoff your design, 5+ chat sidebars with customizable settings, multi-prompt workflow screen designs, 8+ prompt boxes, and dark mode. LanguageGUI is designed with variables and styles, designed with Figma Auto Layout, and is free to use for both personal and commercial projects without required attribution.

Anyscale is a company that provides a scalable compute platform for AI and Python applications. Their platform includes a serverless API for serving and fine-tuning open LLMs, a private cloud solution for data privacy and governance, and an open source framework for training, batch, and real-time workloads. Anyscale's platform is used by companies such as OpenAI, Uber, and Spotify to power their AI workloads.

LiteLLM is a platform that provides model access, logging, and usage tracking across various LLMs in the OpenAI format. It offers features such as control over model access, budget tracking, pass-through endpoints for migration, OpenAI-compatible API access, and a self-serve portal for key management. LiteLLM also offers different pricing tiers, including Open Source, Enterprise Basic, and Enterprise Premium, with various integrations and features tailored for different user needs.

LM Studio is an AI tool designed for discovering, downloading, and running local LLMs (Large Language Models). Users can run LLMs on their laptops offline, use models through an in-app Chat UI or a local server, download compatible model files from HuggingFace repositories, and discover new LLMs. The tool ensures privacy by not collecting data or monitoring user actions, making it suitable for personal and business use. LM Studio supports various models like ggml Llama, MPT, and StarCoder on Hugging Face, with minimum hardware/software requirements specified for different platforms.

Jan is an open-source ChatGPT-alternative that runs 100% offline. It allows users to chat with AI, download and run powerful models, connect to cloud AIs, set up a local API server, and chat with files. Highly customizable, Jan also offers features like creating personalized AI assistants, memory, and extensions. The application prioritizes local-first AI, user-owned data, and full customization, making it a versatile tool for AI enthusiasts and developers.

CloseFactor is an AI-powered GTM Operating System that helps sales teams close more deals by providing tailored opportunities with potential buyers who are ready to make a purchase. The platform utilizes machine learning and data analysis to identify the best-fit accounts, research their buying readiness in real-time, recommend contact engagements based on past successful deals, and automatically generate customized messages using generative AI. CloseFactor also assists in identifying the Ideal Customer Profile (ICP) by analyzing common characteristics of closed-won deals, enabling users to target the right accounts effectively. The tool has shown to increase the quality and quantity of pipeline from target accounts, improve marketing spend optimization, and enhance personalized outreach for better sales outcomes.

Loata is an AI-powered platform that serves as a learning orchestrator for adaptive text analyses. It allows users to store their notes and documents in the cloud, which are then ingested and transformed into knowledge bases. The platform features smart AI agents powered by LLMs to provide intelligent answers based on the content. With end-to-end encryption and controlled ingestion, Loata ensures the security and privacy of user data. Users can choose from different subscription plans to access varying levels of storage and query capacity, making it suitable for individuals and professionals alike.

DigiCord is an AI-powered Discord bot that provides access to a wide range of large language models (LLMs) such as GPT-3.5, GPT-4, Claude, and more. It allows users to converse with AI, generate content, analyze images and data, and perform various tasks, all within the Discord server environment. DigiCord aims to democratize AI tools and technologies, making them more accessible, cost-efficient, and user-friendly for a diverse range of users, from students and digital artists to software engineers and entrepreneurs.

ad:personam is an AI-powered Self Serve DSP platform for programmatic advertising, designed to empower businesses of any size to thrive in the programmatic advertising space. It offers a comprehensive suite of programmatic advertising solutions, cutting-edge AI-driven insights and planning tools, and transparent pricing. With features like multi-format ad uploads, cookieless targeting, and in-depth reporting, ad:personam aims to simplify programmatic advertising with AI efficiency and effectiveness.

BoldDesk by Syncfusion is a comprehensive customer service software designed to effortlessly resolve 70% of customer inquiries. It offers features such as ticketing system, live chat support, omnichannel inbox, knowledge base, task management, and AI Copilot. The application aims to enhance customer service efficiency through automation, collaboration, and customization. BoldDesk provides workflow automation, contact management, reports & analytics, mobile app, customer portal, apps & integrations, and satisfaction survey (CSAT). It caters to startups, small businesses, and enterprises, offering solutions for email ticketing, shared inbox software, multi-brand help desk, internal help desk software, trouble ticketing software, and embedded help widgets.

Backend.AI is an enterprise-scale cluster backend for AI frameworks that offers scalability, GPU virtualization, HPC optimization, and DGX-Ready software products. It provides a fast and efficient way to build, train, and serve AI models of any type and size, with flexible infrastructure options. Backend.AI aims to optimize backend resources, reduce costs, and simplify deployment for AI developers and researchers. The platform integrates seamlessly with existing tools and offers fractional GPU usage and pay-as-you-play model to maximize resource utilization.

Seldon is an MLOps platform that helps enterprises deploy, monitor, and manage machine learning models at scale. It provides a range of features to help organizations accelerate model deployment, optimize infrastructure resource allocation, and manage models and risk. Seldon is trusted by the world's leading MLOps teams and has been used to install and manage over 10 million ML models. With Seldon, organizations can reduce deployment time from months to minutes, increase efficiency, and reduce infrastructure and cloud costs.

Helsing is an AI tool designed to serve and protect democracies by leveraging artificial intelligence technology. The company focuses on developing software-first solutions that enhance defense capabilities and safeguard democratic values. Helsing collaborates with industry and governments to integrate advanced AI with hardware platforms, aiming to achieve technological leadership in the defense sector. The team at Helsing comprises experts with diverse backgrounds in software, defense, intelligence, and artificial intelligence, united by a shared commitment to upholding democratic principles.

Baseten is a machine learning infrastructure that provides a unified platform for data scientists and engineers to build, train, and deploy machine learning models. It offers a range of features to simplify the ML lifecycle, including data preparation, model training, and deployment. Baseten also provides a marketplace of pre-built models and components that can be used to accelerate the development of ML applications.

Meteron AI is an all-in-one AI toolset that helps developers build AI-powered products faster and easier. It provides a simple, yet powerful metering mechanism, elastic scaling, unlimited storage, and works with any model. With Meteron, developers can focus on building AI products instead of worrying about the underlying infrastructure.

Heenok is an AI-powered content-generating tool designed to help users quickly create high-quality content with minimal effort, time, and cost. It offers features such as AI-powered social media marketing, content improvement, video script writing, landing page copy generation, and business strategy development. Heenok's cutting-edge technology leverages artificial intelligence to generate engaging and original content that resonates with the audience. The tool aims to save time and money by automating content creation processes and providing intuitive interfaces for users to create human-like content effortlessly.

The Future of Privacy Forum (FPF) is an AI tool that serves as a catalyst for privacy leadership and scholarship, advancing principled data practices in support of emerging technologies. It provides resources, training sessions, and guidance on AI-related topics, online advertising, youth privacy legislation, and more. FPF brings together industry, academics, civil society, policymakers, and other stakeholders to explore challenges posed by emerging technologies and develop privacy protections, ethical norms, and best practices.

This repository provides a comprehensive list of academic papers, articles, tutorials, slides, and projects related to Large Language Model (LLM) systems. It covers various aspects of LLM research, including pre-training, serving, system efficiency optimization, multi-model systems, image generation systems, LLM applications in systems, ML systems, survey papers, LLM benchmarks and leaderboards, and other relevant resources. The repository is regularly updated to include the latest developments in this rapidly evolving field, making it a valuable resource for researchers, practitioners, and anyone interested in staying abreast of the advancements in LLM technology.

OpenLLM is a platform that helps developers run any open-source Large Language Models (LLMs) as OpenAI-compatible API endpoints, locally and in the cloud. It supports a wide range of LLMs, provides state-of-the-art serving and inference performance, and simplifies cloud deployment via BentoML. Users can fine-tune, serve, deploy, and monitor any LLMs with ease using OpenLLM. The platform also supports various quantization techniques, serving fine-tuning layers, and multiple runtime implementations. OpenLLM seamlessly integrates with other tools like OpenAI Compatible Endpoints, LlamaIndex, LangChain, and Transformers Agents. It offers deployment options through Docker containers, BentoCloud, and provides a community for collaboration and contributions.

TensorRT-LLM is an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM contains components to create Python and C++ runtimes that execute those TensorRT engines. It also includes a backend for integration with the NVIDIA Triton Inference Server; a production-quality system to serve LLMs. Models built with TensorRT-LLM can be executed on a wide range of configurations going from a single GPU to multiple nodes with multiple GPUs (using Tensor Parallelism and/or Pipeline Parallelism).

This repository provides resources, demos, and recipes for working with Large Language Models (LLMs) on OpenShift using OpenShift AI or Open Data Hub. It includes instructions for deploying inference servers for LLMs, such as vLLM, Hugging Face TGI, Caikit-TGIS-Serving, and Ollama. Additionally, it offers guidance on deploying serving runtimes, such as vLLM Serving Runtime and Hugging Face Text Generation Inference, in the Single-Model Serving stack of Open Data Hub or OpenShift AI. The repository also covers vector databases that can be used as a Vector Store for Retrieval Augmented Generation (RAG) applications, including Milvus, PostgreSQL+pgvector, and Redis. Furthermore, it provides examples of inference and application usage, such as Caikit, Langchain, Langflow, and UI examples.

Scale's LLM Engine is an open-source Python library, CLI, and Helm chart that provides everything you need to serve and fine-tune foundation models, whether you use Scale's hosted infrastructure or do it in your own cloud infrastructure using Kubernetes.

LitServe is a high-throughput serving engine designed for deploying AI models at scale. It generates an API endpoint for models, handles batching, streaming, and autoscaling across CPU/GPUs. LitServe is built for enterprise scale with a focus on minimal, hackable code-base without bloat. It supports various model types like LLMs, vision, time-series, and works with frameworks like PyTorch, JAX, Tensorflow, and more. The tool allows users to focus on model performance rather than serving boilerplate, providing full control and flexibility.

LLM-on-Ray is a comprehensive solution for building, customizing, and deploying Large Language Models (LLMs). It simplifies complex processes into manageable steps by leveraging the power of Ray for distributed computing. The tool supports pretraining, finetuning, and serving LLMs across various hardware setups, incorporating industry and Intel optimizations for performance. It offers modular workflows with intuitive configurations, robust fault tolerance, and scalability. Additionally, it provides an Interactive Web UI for enhanced usability, including a chatbot application for testing and refining models.

The GenAI repository contains pipelines that implement image and text generation tasks. The implementation uses OpenVINO capabilities to optimize the pipelines. Each sample covers a family of models and suggests certain modifications to adapt the code to specific needs. It includes the following pipelines: 1. Benchmarking script for large language models 2. Text generation C++ samples that support most popular models like LLaMA 2 3. Stable Diffuison (with LoRA) C++ image generation pipeline 4. Latent Consistency Model (with LoRA) C++ image generation pipeline

Candle-vllm is an efficient and easy-to-use platform designed for inference and serving local LLMs, featuring an OpenAI compatible API server. It offers a highly extensible trait-based system for rapid implementation of new module pipelines, streaming support in generation, efficient management of key-value cache with PagedAttention, and continuous batching. The tool supports chat serving for various models and provides a seamless experience for users to interact with LLMs through different interfaces.

Lingo is a lightweight ML model proxy that runs on Kubernetes, allowing you to run text-completion and embedding servers without changing OpenAI client code. It supports serving OSS LLMs, is compatible with OpenAI API, plug-and-play with messaging systems, scales from zero based on load, and has zero dependencies. Namespaced with no cluster privileges needed.

KG-LLM-Papers is a repository that collects papers integrating knowledge graphs (KGs) and large language models (LLMs). It serves as a comprehensive resource for research on the role of KGs in the era of LLMs, covering surveys, methods, and resources related to this integration.

Since the emergence of chatGPT in 2022, the acceleration of Large Language Model has become increasingly important. Here is a list of papers on LLMs inference and serving.

This repository compiles a list of academic papers that evaluate, align, simulate, and provide surveys or perspectives on the use of Large Language Models (LLMs) in the field of Social Science. The papers cover various aspects of LLM research, including assessing their alignment with human values, evaluating their capabilities in tasks such as opinion formation and moral reasoning, and exploring their potential for simulating social interactions and addressing issues in diverse fields of Social Science. The repository aims to provide a comprehensive resource for researchers and practitioners interested in the intersection of LLMs and Social Science.

A comprehensive guide to building Retrieval Augmented Generation (RAG)-based LLM applications for production. This guide covers developing a RAG-based LLM application from scratch, scaling the major components, evaluating different configurations, implementing LLM hybrid routing, serving the application in a highly scalable and available manner, and sharing the impacts LLM applications have had on products.

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

FastServe-AI is a machine learning serving tool focused on GenAI & LLMs with simplicity as the top priority. It allows users to easily serve custom models by implementing the 'handle' method for 'FastServe'. The tool provides a FastAPI server for custom models and can be deployed using Lightning AI Studio. Users can install FastServe-AI via pip and run it to serve their own GPT-like LLM models in minutes.

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