knowledge-distillation

Compress a large language model into a smaller student model using knowledge distillation techniques like temperature scaling, soft targets, and reverse KLD. This transfers capabilities from a teacher model to a smaller one for reduced inference costs.

Yes, you can distill knowledge from a 70B parameter model to a 7B model. This model compression process retains over 90% of the original performance while significantly lowering inference costs for resource-constrained deployments.

Supported knowledge distillation techniques for model transfer include temperature scaling, soft targets, reverse KLD (MiniLLM), logit distillation, and response distillation to efficiently transfer capabilities from teacher to student models.

Yes, you need PyTorch and DeepSpeed along with transformers, datasets, accelerate, and wandb dependencies to execute the knowledge distillation scripts. These frameworks provide the necessary environment for training and model compression.

Transfer capabilities from proprietary models to open-source alternatives using response distillation and logit distillation. This knowledge transfer method uses the proprietary model as a teacher to train an efficient open-source student model.

Reverse KLD (MiniLLM) for LLM compression requires significant computational resources and careful hyperparameter tuning, such as adjusting temperature and alpha values, to prevent mode collapse and ensure effective knowledge transfer to the student model.