Tutorials

The following tutorials walk through the main flows of MASE, from model training to deployment.

Hint

For a quick introduction, see the Quickstart page. The tutorials on this page dive deeper into various features and use cases.

Core Tutorials

The following tutorials show how to use Mase for standard model compression and optimization flows, such as Finetuning, Quantization and Pruning.

• Tutorial 1: Introduction to the Mase IR, MaseGraph and Torch FX passes
• Tutorial 2: Finetuning Bert for Sequence Classification using a LoRA adapter
• Tutorial 3: Running Quantization-Aware Training (QAT) on Bert
• Tutorial 4: Unstructured Pruning on Bert

Architecture Search

The following show how to effectively run Neural Architecture Search (NAS) and mixed-precision search to find optimal architecture configurations for a given task or dataset.

• Tutorial 5: Neural Architecture Search (NAS) with Mase and Optuna
• Tutorial 6: Mixed Precision Quantization Search with Mase and Optuna

Post-Training Quantization

The following tutorial walks through Mase’s MX post-training quantization flow end to end, mirroring the PLENA Software walk-through.

• Tutorial 7: MX Post-Training Quantization with Mase

Developer Guide

If you’d like to contribute to Mase, you may find the following resources useful.

• Developer: Guide on how to add a new model into Chop
• Developer: How to write documentations in MASE
• Developer: How to extend search