Training
Transformer
The neural network architecture behind virtually all modern large language models.
In depth
The neural network architecture behind virtually all modern large language models. Introduced in the 2017 paper "Attention Is All You Need," transformers use self-attention mechanisms to process all tokens in a sequence simultaneously rather than one at a time. This parallelism makes them vastly more efficient to train than previous architectures like RNNs and LSTMs, and is the reason LLMs were able to scale to billions of parameters.
Example
This parallelism makes them vastly more efficient to train than previous architectures like RNNs and LSTMs, and is the reason LLMs were able to scale to billions of parameters.
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Hands-on guides, comparisons, and tutorials that cover Training.
FAQ
Common questions
What is Transformer?
The neural network architecture behind virtually all modern large language models.
Why does Transformer matter for AI developers?
Transformer sits in the Training part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Transformer?
Developers Digest publishes tutorials and videos that cover Training topics including Transformer. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Training
Transfer Learning
The technique of taking a model trained on one task and adapting it for a different but related task.
Training
Expert Parallelism
A distributed training and inference strategy for Mixture of Experts models where different experts live on different GPUs instead of every device holding a full copy.
Training
Synthetic Data
Training data generated by AI models rather than collected from real-world sources.
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