Research on lightweight optimization of large language models for resource-constrained environments

This study attempts to explore and implement a lightweight optimization strategy for large language models in resource-constrained environments. Specifically, it uses model distillation technology to optimize the computational efficiency and storage requirements of large language models. It selects a typical optimization algorithm, model distillation, and constructs an optimization framework. It uses actual data sets to conduct experiments to verify the results. The experimental results show that after distillation optimization, the accuracy of the student model is close to that of the teacher model. At the same time, it greatly reduces the number of parameters, inference time and memory usage, and greatly improves the inference effect of resource-constrained platforms. Compared with traditional optimization methods such as pruning and quantization, the distillation method can not only effectively reduce the model size, but also better maintain accuracy, thereby improving inference performance. The research results show that distillation technology has outstanding advantages in the lightweight optimization of large language models and is suitable for deployment on various low-resource platforms.