01-10-2026, 08:21 AM
Self-Attention: This core mechanism lets the model focus on different parts of the input text to understand context, figuring out which words are most relevant to each other, even if they're far apart.
Parallel Processing: Unlike older models that processed words one by one, Transformers process entire sequences at once, drastically speeding up training on massive datasets.
Encoder-Decoder Structure: They typically use encoders to understand input and decoders to generate output, though some LLMs use only decoder-style blocks.
Tokens: Text is broken down into "tokens" (words or sub-words) that are converted into numerical vectors, allowing the model to process language mathematically.
Key Characteristics of LLMs
Massive Scale: LLMs have billions of parameters and are trained on enormous amounts of text and data from the internet, books, and more.
Pre-training & Fine-tuning: They learn general language patterns during broad pre-training and can then be specialized (fine-tuned) for specific tasks.
Generative: They predict the next most likely token, allowing them to generate coherent and creative text, code, or even images and audio.
Watch this video for a visual explanation of the Transformer model:
https://youtu.be/k1ILy23t89E?si=UlwtKorH1rEkhEDM
Parallel Processing: Unlike older models that processed words one by one, Transformers process entire sequences at once, drastically speeding up training on massive datasets.
Encoder-Decoder Structure: They typically use encoders to understand input and decoders to generate output, though some LLMs use only decoder-style blocks.
Tokens: Text is broken down into "tokens" (words or sub-words) that are converted into numerical vectors, allowing the model to process language mathematically.
Key Characteristics of LLMs
Massive Scale: LLMs have billions of parameters and are trained on enormous amounts of text and data from the internet, books, and more.
Pre-training & Fine-tuning: They learn general language patterns during broad pre-training and can then be specialized (fine-tuned) for specific tasks.
Generative: They predict the next most likely token, allowing them to generate coherent and creative text, code, or even images and audio.
Watch this video for a visual explanation of the Transformer model:
https://youtu.be/k1ILy23t89E?si=UlwtKorH1rEkhEDM