A look under the bonnet of the transfomer, the Motor -Fahr -KI model development

Today, practically every state -of -the -art AI product and model uses a transformer architecture. Great -speaking models (Llms) like GPT-4O, Lama, Gemini and Claude are all transformer-based and other AI technology.

Since the hype around Ai is probably not slowed down soon, it is time to give transformers your guilt. Therefore, I would like to explain a little about how to work why you are so important for growth scalable solutions and why you are the backbone of LLMS.

Transformers are more than the eye

In short, a transformer is a neuronal network architecture with which data sequences are to be modeled, which means that they are ideal for tasks such as voice translation, sentence, automatic speech recognition and much more. For many of these sequence modeling tasks, transformers have really become a dominant architecture Training and implementation of inference.

Originally introduced in a paper from 2017, “Attention is everything you need”The transformer was introduced by Google researchers as an encoder decoder architecture, which was specially developed for language translation. The following year, Google published Bidirectional Encoder representations of Transformers (Bert), which could be considered one of the first LLMs – even though today’s standards are considered small.

Since then and particularly accelerated with the advent of GPT models of Openai – The trend was to train ever larger models with more data, more parameters and longer context windows.

To make this development easier, there were many innovations such as: more advanced GPU hardware and better software for multi-GPU training; Techniques such as quantization and mixture of experts (MOE) to reduce memory consumption; New optimizers for training, such as shampoo and Adamw; Techniques for the efficient calculation of attention such as flash stance and KV caching. The trend will probably continue for the foreseeable future.

The importance of self -relationship in transformers

Depending on the application, an encoder decoder architecture follows. The Encoder component learns a vectordinance of data, which can then be used for downstream tasks such as classification and mood analysis. The decoder component takes on a vector or a latent representation of the text or the image and generates it to generate new text, which makes it useful for tasks such as sentence and summary. For this reason, many well-known state-of-the-art models, such as the GPT family, are just decoders.

Encoder decoder models combine both components and make them useful for the translation and other tasks of sequence-to-sequence. The core component is the attention layer for both encoder and decoder architectures, since a model retains a context of words that appear in the text much earlier.

Attention takes place in two flavors: self -fighting and cross movement. Self -fighting is used to absorb relationships between words within the same sequence, while a cross -compliance is used to absorb relationships between words between two different sequences. Cross-tentures combine encoder and decoder components in a model and during translation. For example, the English word “strawberry” enables the French word “fraise”. Mathematical are both self -fighting and cross -fighting different forms of matrix multiplication, which can be carried out extremely efficient using a GPU.

Due to the attention layer, transformers can be the relationships between words that are separated by long text quantities in the text.

The future of the models

Transformers are currently the dominant architecture for many applications that require LLMS and benefit from the greatest research and development. Although this does not seem to change in so soon, another class of model that has been interested in interest has recently been interested in state space models (SSMS) like Mamba. This highly efficient algorithm can process very long data sequences, while transformers are limited by a context window.

For me, the most exciting applications of transformer modelle multimodal models are. Openais GPT-4O, for example, is able to follow text, audio and images-and other providers to follow. Multimodal applications are very diverse and range from video signature to language clones to image segmentation (and more). They also offer the opportunity to make AI more accessible to people with disabilities. For example, a blind could be served significantly due to the ability to interact with a multimodal application through language and audio components.

It is an exciting room with a lot of potential to uncover new applications. But remember that at least for the foreseeable future is largely underpinned by transformer architecture.

Terrence Alsup is a senior data scientist Finastra.