A Transformer Becomes an LLM
The article discusses the process of transforming a transformer architecture into a large language model, highlighting the importance of training and customization. It explains how a stack of transformer layers is not yet a functional model, but rather a pile of random numbers that requires training to become useful. The article outlines the steps involved in training a large language model, including pre-training, supervised fine-tuning, and alignment.
- ▪A stack of transformer layers is not yet a functional model, but rather a pile of random numbers that requires training to become useful.
- ▪The process of transforming a transformer architecture into a large language model involves pre-training, supervised fine-tuning, and alignment.
- ▪The model is trained on trillions of tokens, which are the basic units of text, rather than words.
Opening excerpt (first ~120 words) tap to expand
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Bharad.
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