Jerry Adams Franklin

Fine-tuning large language models in resource-constrained environments: Methods and trade-offs

The rapid scaling of large language models has created significant barriers to fine-tuning, with standard approaches requiring computational resources far exceeding typical hardware constraints. A 7-billion parameter model demands roughly 100 to 120GB of memory for full fine-tuning under mixed-precision training, effec...