Reducing the precision of a model's numerical values — for example, from 32-bit to 4-bit numbers.
Reducing the precision of a model's numerical values — for example, from 32-bit to 4-bit numbers. This shrinks the model size dramatically and speeds up inference with minimal quality loss. Essential for running large models on consumer hardware. GGUF and GPTQ are popular quantization formats.
Running a trained model to make predictions on new data.
Low-Rank Adaptation.
A mathematical function applied to a neuron's output that introduces non-linearity into the network.
An optimization algorithm that combines the best parts of two other methods — AdaGrad and RMSProp.
Artificial General Intelligence.
The research field focused on making sure AI systems do what humans actually want them to do.
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