Model Optimization Techniques

Optimizing large language models involves various techniques such as pruning, quantization, and knowledge distillation. These methods help reduce the model size and improve inference speed without significantly sacrificing performance. Understanding these techniques is essential for developers looking to deploy LLMs in resource-constrained environments.