Jun 27, 2018 · In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained. The Transformer outperforms the Google Neural Machine Translation model in specific tasks.

The GPT2 was, however, a very large, transformer-based language model trained on a massive dataset. In this post, we’ll look at the architecture that enabled the model to produce its results. We will go into the depths of its self-attention layer. And then we’ll look at applications for the decoder-only transformer beyond language modeling.

Jan 3, 2022 · This article breaks down DeepMind’s RETRO (Retrieval-Enhanced TRansfOrmer) and how it works. The model performs on par with GPT-3 despite being 4% its size (7.5 billion parameters vs. 185 billion for GPT-3 Da Vinci).

Jan 27, 2025 · R1 uses the base model (not the final DeepSeek-v3 model) from that previous paper, and still goes through an SFT and preference tuning steps, but the details of how it does them are what's different. There are three special things to highlight in the R1 creation process.

Thanks for taking the time to check it out :) Topics include: The Narrated Transformer Language Model Recurrent Neural Networks (RNNs), Clearly Explained!!! ...

Ecco: An Open Source Library for the Explainability of Transformer Language Models J Alammar Proceedings of the 59th Annual Meeting of the Association for Computational … , 2021

May 21, 2023 · Fortunately, a team of researchers, led by Jay Alammar, created “The Illustrated Transformer,” a visually appealing and intuitive guide that elucidates the inner workings of this powerful...

Nov 18, 2024 · Chapter 1 provides a high-level summary of the inner workings of language models, while Chapters 2 and 3 break down these concepts further. The authors skillfully use diagrams, illustrations, and...

You'll understand how to use pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; and use existing libraries and pretrained models for text classification, search, and clusterings. This book also helps you: 1. An Introduction to Large Language Models.

Interfaces for exploring transformer language models by looking at input saliency and neuron activation. The Transformer architecture [1] has been powering a number of the recent advances in NLP. A breakdown of this architecture is provided here [2].