This article explains how to run inference on a YOLOv8 object detection model using Docker and create a REST API to orchestrate the process. It includes code implementation and a detailed README in the author's GitHub repository for running the API via REST with Docker.
OnDemand AI provides API services for media, services, and plugins, allowing developers to upload media, use NLP, and deploy machine learning models. It also facilitates serverless application deployment and allows BYOM (Bring Your Own Model) and BYOI (Bring Your Own Inference).
This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.
Official Beepy API documentation providing information about getting started, installation, authentication, IP whitelist, request parameters, response examples, webhooks, labels, errors, and changelogs.
Learn how to build an open LLM app using Hermes 2 Pro, a powerful LLM based on Meta's Llama 3 architecture. This tutorial explains how to deploy Hermes 2 Pro locally, create a function to track flight status using FlightAware API, and integrate it with the LLM.
This guide provides an introduction to kubectl, the command-line tool used to communicate with the Kubernetes API. It covers command syntax, useful commands, flags, and tips and tricks. It also discusses the ecosystem of plugins and tools built to expand the functionalities of kubectl and Kubernetes.
A tutorial showing you how how to bring real-time data to LLMs through function calling, using OpenAI's latest LLM GTP-4o.
- Introduces embeviz - a simple side project for exploring text embeddings
- Uses backend API with GoFiber framework and frontend UI with React and React Router
- Provides interactive charts for visualizing computed projections
- Can label texts and select options for both projections and chunking
- Offers swagger docs for the API, in-memory data store, and persistent data store with QDrant
- Uses backend API with GoFiber framework and frontend UI with React and React Router
- Provides interactive charts for visualizing computed projections
- Can label texts and select options for both projections and chunking
- Offers swagger docs for the API, in-memory data store, and persistent data store with QDrant
The authors map the landscape of frameworks for abstracting interactions with and between large language models, and suggest two systems of organization for reasoning about the various approaches to, and philosophies of, LLM abstraction.
