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Borealis AI has recently released a collection of comprehensive tutorials on Large Language Models and Transformers. These tutorials serve as a valuable resource for individuals seeking to expand their knowledge and understanding of these technologies. Whether you are a beginner looking for a high-level introduction or an experienced professional seeking in-depth technical knowledge, these tutorials cater to a diverse range of learning needs.
A High-level Overview of Large Language Models
This tutorial serves as an introductory blog post aimed at individuals with no prior background in language models. It provides a high-level overview of large language models, offering a general understanding of the topic.
Transformers I: Introduction
In today’s modern language models, the transformer architecture plays a crucial role. This tutorial provides a comprehensive introduction to transformers, diving into the intricacies of this architecture. The blog covers essential concepts such as the self-attention mechanism, including variants like scaled self-attention, multi-head self-attention, masked self-attention, and cross-attention. Additionally, it explores how position encodings are incorporated into transformers. To illustrate these concepts, the tutorial delves into the encoder, decoder, and encoder-decoder transformer models, using popular examples like BERT, GPT3, and automatic translation.
Transformers II: Extensions
Building upon the foundation laid in the previous tutorial, this blog post delves deeper into transformers by discussing various extensions. Firstly, it explores different methods of incorporating position information into transformers. Additionally, the tutorial covers how transformers can handle longer sequence lengths by extending the self-attention mechanism. Lastly, it examines the relationship between transformers and other architectures, including RNNs, convolutional networks, gating networks, and hypernetworks.
Transformers III: Training
Training transformers can be a nuanced process, often more complex than training other architectures. This tutorial delves into the subtleties of training transformers, shedding light on the challenges involved. It explores the impact of self-attention architecture, layer normalization, and residual links on activation variance, which can make training more challenging. The tutorial also introduces various techniques and tricks, such as learning rate warm-up, to overcome these challenges.
Neural Natural Language Generation
Decoding from neural models, such as transformers, is a topic that is rarely discussed. This tutorial addresses this gap by tackling the intricacies of neural natural language generation. It explores the choices made when combining tokens to form the final sentence, discussing methods such as top-k sampling, nucleus sampling, beam search, and diverse beam search.
Training and Fine-tuning Large Language Models
Training and fine-tuning large language models play a crucial role in applications like chatbots. This tutorial focuses on training and fine-tuning these models, using examples like Chat-GPT. It covers topics such as model pre-training, few-shot learning, supervised fine-tuning, reinforcement learning from human feedback (RLHF), and direct preference optimization. These techniques enable the creation of powerful and effective chatbot models.
Speeding up Inference in Transformers
As the output of a transformer depends on all preceding tokens, inference in language models can become slower as the output length increases. This tutorial addresses the challenge of speeding up inference in transformers by exploring various techniques and mechanisms. It delves into variations of the self-attention mechanism that enhance efficiency, including attention-free transformers, RWKV, linear transformers, Performers, and the recent “retentive network.”
In conclusion, Borealis AI’s comprehensive tutorials on Large Language Models and Transformers provide a wealth of knowledge and insights into these technologies. Whether you are a beginner or an experienced practitioner, these tutorials offer a valuable resource to enhance your understanding. Explore the collection and delve into the world of large language models and transformers.
