Fine-Tuning
Fine-Tuning is the process of taking a pre-trained model and training it further on a smaller, specific dataset to adapt it for a particular task or domain. Fine-tuning alters the internal weights of the network, specializing its behavior and tone.
Frequently Asked Questions
What is the difference between pre-training and fine-tuning?▼
Pre-training is the massive, expensive initial training to teach the model general language. Fine-tuning is secondary training to specialize it.
Can I run fine-tuning on a consumer GPU?▼
Yes, using parameter-efficient fine-tuning (PEFT) methods like LoRA (Low-Rank Adaptation), which lock most weights and train only a small fraction.
Fine-Tuning Media Coverage & Intelligence
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Fine-tuning for domain-specific tasks means improving performance in one area without degrading the model's general capabilities, and getting that balance right