Category: AI/ML Research

Editor’s note: This article is a part of our series on visualizing the foundations of machine learning.

When I first started reading machine learning research papers, I honestly thought something was wrong with me.

Embeddings — vector-based numerical representations of typically unstructured data like text — have been primarily popularized in the field of natural language processing (NLP).

Large language models like LLaMA, Mistral, and Qwen have billions of parameters that demand a lot of memory and compute power.

Editor’s note: This article is a part of our series on visualizing the foundations of machine learning.

Most ChatGPT users don’t know this, but when the model searches the web for current information or runs Python code to analyze data, it’s using tool calling.

This article is divided into four parts; they are: • The Reason for Fine-tuning a Model • Dataset for Fine-tuning • Fine-tuning Procedure • Other Fine-Tuning Techniques Once you train your decoder-only transformer model, you have a text generator.

The agentic AI field is moving from experimental prototypes to production-ready autonomous systems.

Editor’s note: This article is a part of our series on visualizing the foundations of machine learning.

This article is divided into five parts; they are: • An Example of Tensor Parallelism • Setting Up Tensor Parallelism • Preparing Model for Tensor Parallelism • Train a Model with Tensor Parallelism • Combining Tensor Parallelism with FSDP Tensor parallelism originated from the Megatron-LM paper.