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This involves removing duplicates, filtering out low-quality "gibberish" text, and stripping away PII (Personally Identifiable Information). 3. Training Infrastructure and Hardware
Reduces memory usage and speeds up training without significantly sacrificing accuracy. build a large language model from scratch pdf
The model learns to predict the next token in a sequence using an unsupervised approach. This is where it gains "world knowledge." This involves removing duplicates
(Note: This is a placeholder for your internal resource link) Conclusion filtering out low-quality "gibberish" text
This is the "expensive" part of building an LLM from scratch.