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AI Ecosystem

About

Geniusrise is a modular, loosely-coupled MLOps framework designed for the era of Large Language Models, offering flexibility and standardization in designing networks of AI agents.

It defines components and orchestrates them providing observability, state management and data handling, all while supporting diverse infrastructures. With its modular and unopinonated architecture, Geniusrise empowers teams to build, share, and deploy AI across various platforms.

Geniusrise is powered by its components:

Links

Examples

This repository is a comprehensive collection of examples demonstrating the versatility and power of our software across three core functionalities:

  1. Hosting APIs with Any Model: Learn how to deploy a variety of models as APIs, enabling easy access and interaction.
  2. Performing Bulk Inference with Any Model: Discover methods to process large amounts of data in batches, leveraging any model for efficient inference.
  3. Fine-Tuning with Any Model and Any Dataset: Explore how to customize models to your specific needs using various datasets, enhancing their performance and accuracy.

Each example is designed to provide practical, real-world applications, guiding you through the complexities of model deployment, bulk processing, and fine-tuning.

Directory Structure

  • ./cli: Contains command-line usage examples for each of the core functionalities. These examples are intended to showcase how to interact with our software via the command line, providing a hands-on experience in executing various tasks.

  • ./yaml: Accompanies the CLI examples with YAML configuration files. These files are crucial for understanding the setup and parameters needed for each example, offering a template for customization and experimentation.

Getting Started

To begin using these examples, clone this repository to your local environment. Ensure you have geniusrise and its components installed.

For each example, navigate to the corresponding directory in the CLI or YAML folders and use the examples in each individual .sh file.