Full Fine-Tuning the traditional method for adapting large language models (LLMs), involving updates to all of the model’s parameters. While more resource-intensive than parameter-efficient fine-tuning (PEFT) and other methods, it allows for deeper and more comprehensive customization, especially when adapting to significantly different tasks or domains.
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Artificial Intelligence (AI) and Machine Learning (ML) require a lot of computing power. Specifically, you would want to have GPUs which have become the standard tool for computing-intensive applications because of their parallel processing capabilities, high throughput, and efficiency in handling the kinds of large-scale computations required for AI and ML.
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