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Training
LoRA / QLoRA
Parameter-efficient fine-tuning methods that only update a small fraction of weights.
LoRA (Low-Rank Adaptation) trains small adapter matrices that are added to a frozen base model, instead of updating all the model's weights. Result: vastly cheaper training, smaller artifacts to deploy (megabytes instead of gigabytes), and the ability to swap adapters in/out for different tasks on the same base model. QLoRA is LoRA on a quantized (4-bit) base model, which makes fine-tuning large open-weight models possible on a single GPU.
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Building with LoRA / QLoRA?
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