Tag: LoRA
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QLoRA Explained: A 4-Bit Base, NF4, and What It Really Costs
QLoRA puts a 7B to 30B fine-tune on one card. NF4, double quantisation, paged optimizers, the throughput tax nobody mentions, and the…
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LoRA Explained: Freeze the Model, Learn a Low-Rank Patch
LoRA fine-tuning from the mechanic up: the low-rank update, the zero initialisation, rank and alpha, the honest comparison, and merging versus adapter…
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Training Memory: The Four Tenants and the 16 Bytes Per Parameter
Why a 3 GB model needs 25 GB to train. The four memory tenants derived from first principles, where the 16 bytes…
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LLM Fine-Tuning Explained: What Actually Changes Inside the Model
LLM fine-tuning runs the pretraining objective on your data with a loss mask. What actually changes, when to do it, and how…