Categories: AI/ML Research

Training a Model with Limited Memory using Mixed Precision and Gradient Checkpointing

This article is divided into three parts; they are: • Floating-point Numbers • Automatic Mixed Precision Training • Gradient Checkpointing Let’s get started! The default data type in PyTorch is the IEEE 754 32-bit floating-point format, also known as single precision.

Train Your Large Model on Multiple GPUs with Pipeline Parallelism

This article is divided into six parts; they are: • Pipeline Parallelism Overview • Model Preparation for Pipeline Parallelism • Stage and Pipeline Schedule • Training Loop • Distributed Checkpointing • Limitations of Pipeline Parallelism Pipeline parallelism means creating the model as a pipeline of stages.

December 30, 2025

In "AI/ML Research"

Train Your Large Model on Multiple GPUs with Fully Sharded Data Parallelism

This article is divided into five parts; they are: • Introduction to Fully Sharded Data Parallel • Preparing Model for FSDP Training • Training Loop with FSDP • Fine-Tuning FSDP Behavior • Checkpointing FSDP Models Sharding is a term originally used in database management systems, where it refers to dividing…

December 31, 2025

In "AI/ML Research"

Using Quantized Models with Ollama for Application Development

Quantization is a frequently used strategy applied to production machine learning models, particularly large and complex ones, to make them lightweight by reducing the numerical precision of the model’s parameters (weights) — usually from 32-bit floating-point to lower representations like 8-bit integers.

May 30, 2025

In "AI/ML Research"

AI Generated Robotic Content