Transformer bonnet, underneath the engine that drives the evolution of AI models

Today, virtually every cutting-edge AI product and model uses transformer architectures. Large-scale language models (LLMs) such as GPT-4O, LLAMA, GEMINI, and CLAUDE are all transformers based, and transformers can be used to include other AI applications such as text-to-speech, automatic speech recognition, image generation, and inter-text models. There are as their fundamental techniques.

It’s not likely that the hype around AI will soon be slower, so it’s time to give transformers their deadlines, so how do they work, why are they so important to the growth of scalable solutions? , and I want to explain a bit about why they are important, and they are the backbone of LLMS.

Transformers are more than just eye contact

Simply put, transformers are neural network architectures designed to model sequences of data, perfect for tasks such as language translation, sentence completion, and automatic speech recognition. Trans has become a truly dominant architecture for many of these sequence modeling tasks. This allows for easy parallelization of the underlying attention mechanics, allowing it to be done on a large scale when training and performing inferences.

It was originally featured in a 2017 paper.Care must be taken“From Google researchers, The Transformer was introduced as an encoder decoder architecture specially designed for language translation. The following year, Google released a bidirectional encoder representation from Transformers (BERT). This can be considered one of the first LLMs, but is considered small by today’s standards.

Since then, accelerated with the emergence of GPT models, especially from Openai – has been to train larger models with more data, more parameters, longer context windows.

There are many innovations to promote this evolution, including: More advanced GPU hardware and better software for multi-GPU training. Techniques such as quantization and expert (MOE) mixing to reduce memory consumption. New optimizers for training such as Shampoo and Adamw. Techniques for efficient calculation of attention, such as Flashattention and KV Caching. Foreseeable future trends will likely continue.

The importance of self-joints in transformers

Depending on the application, the transformer model follows the encoder decoder architecture. The encoder component learns a vector representation of data that can be used for downstream tasks such as classification and sentiment analysis. The decoder component retrieves a vector or potential representation of a text or image, and uses it to generate new text, which is useful for tasks such as completing or summarizing sentences. For this reason, many cutting-edge models, such as the GPT family, are decoders alone.

The encoder decoder model combines both components to help you with translation and other sequence-to-sequence tasks. For both the encoder and decoder architectures, the core components are the attention layer. This allows the model to retain context from words that appear long before the text.

There are two flavors to note. Autojoints are used to capture relationships between words within the same sequence, whereas mutual participation is used to capture relationships between words across two different sequences. Mutual participation connects the encoder and decoder components during modeling and translation. For example, the English word “strawberry” can be associated with the French word “Fraise.” Mathematically, both auto-joints and mutual recognition are different forms of matrix growth that can be done very efficiently using the GPU.

Because of the attention layer, transformers can better capture relationships between words separated by long amounts of text, but previous models such as recurrent neural networks (RNNs) and long-term short-term memory (LSTM) models are Lose tracking of previous word context. In text.

The future of models

Currently, Trans is the dominant architecture for many use cases that require LLMS and benefit from the most research and development. Although this seems unlikely to change anytime soon, one of the different classes of models that have recently attracted interest is the state-space models (SSMs), such as MAMBA. This highly efficient algorithm can handle very long sequences of data, but the trans is limited by the context window.

For me, the most exciting application of the transformer model is the multimodal model. For example, Openai’s GPT-4O can process text, audio and images. Other providers are beginning to follow. Multimodal applications are very diverse, ranging from video captions to audio cloning, image segmentation (and more). They also present opportunities to make AI more accessible to people with disabilities. For example, blind people can be greatly helped by their ability to interact through voice and audio components in multimodal applications.

This is an exciting space with plenty of potential to reveal new use cases. But remember, at least in the near future, it will be primarily supported by trans-architectures.

Terrence Alsup is a senior data scientist finastra.