deepspeedai/DeepSpeedExamples

Example models using DeepSpeed

What it solves

This repository provides a centralized collection of practical implementations and benchmarks to help users leverage the DeepSpeed library for large-scale AI model development.

How it works

It organizes a variety of reference implementations across five key areas: training and fine-tuning, high-performance inference (via DeepSpeed-MII and FastGen), model compression, end-to-end applications, and performance benchmarks.

Who it’s for

Developers and researchers who are using DeepSpeed to train, optimize, or deploy large models and need concrete examples of how to implement these features.

Highlights

  • Comprehensive examples for both training and fine-tuning.
  • Dedicated inference paths for DeepSpeed-MII, FastGen, and Hugging Face integrations.
  • Resources for model compression and performance benchmarking.

Related

  • Project
    deepspeedai/DeepSpeed

    A deep learning optimization library that enables the training of models with trillions of parameters by optimizing memory and compute efficiency across distributed systems.

  • Project
    openvinotoolkit/open_model_zoo

    A collection of optimized pre-trained deep learning models and tools for accelerating the development and deployment of high-performance inference applications.

  • Project
    CerebriumAI/examples

    A collection of ready-to-use deployment examples for the Cerebrium platform, covering LLMs, voice agents, image generation, and inference optimization.

  • Project
    bghira/SimpleTuner

    A comprehensive training framework for fine-tuning image, video, and audio generative models, featuring a user-friendly web UI and enterprise-grade orchestration.

  • Project
    jolibrain/deepdetect

    A deep learning runtime and REST server that provides a unified API for training and inference across images, text, and tabular data.