Making AI Work: Fine-Tuning, Inference, Memory | Sharon Zhou, CEO, Lamini
The MAD Podcast with Matt Turck
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Memory Tuning for Model Accuracy
Memory tuning is crucial for ensuring that a model's predictions align accurately with specific facts, reducing the risk of even slightly incorrect outputs. This technique optimizes the model's memory to minimize errors and computational costs, especially in the context of open-source models like Laura, Q-Lora, and others. By fine-tuning a mixture of expert adapters on top of the model, memory tuning enhances the accuracy of predictions by minimizing losses and ensuring that the model selects the most appropriate expert for each scenario.
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