Model Training
Parameter-Efficient Fine-Tuning (PEFT)
Fine-tuning a foundation model by updating only a small fraction of its parameters, faster, cheaper, and nearly as good.
Definition
A family of techniques (LoRA, QLoRA, adapters) that enable fine-tuning large models by modifying only a small subset of parameters. Dramatically reduces compute and memory requirements.
Why it matters
Made it possible for companies to customize billion-parameter models on a single GPU.
Related terms in Model Training
Adversarial Training
Teaching an AI to defend itself by constantly attacking it with tricky or malicious inputs during training.
Autoencoders
A neural network that learns to compress data into a small code and then unzip it back to the original.
Distillation (Model Distillation)
Teaching a small, fast AI model to mimic a large, expensive one, so you get similar results at a fraction of the cost.
Dropout
Randomly turning off some neurons during training so the AI doesn't over-memorize and can generalize better.
From vocabulary to outcomes
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