Fine-Tuning Image-to-Text algorithms with LORA
In the field of large language models, the challenge of fine-tuning has long perplexed researchers. Microsoft, however, has unveiled an innovative solution called Low-Rank Adaptation (LoRA). With the emergence of behemoth models like GPT-3 boasting billions of parameters, the cost of fine-tuning them for specific tasks or domains has become exorbitant. LoRA offers a groundbreaking approach by freezing the weights of pre-trained models and introducing trainable layers known as rank-decomposition matrices in each transformer block. This ingenious technique significantly reduces the number of trainable parameters and minimizes GPU memory requirements, as gradients no longer need to be computed for the majority of model weights.
