knowledge-distillation

Distill a large language model by training a compact student using soft targets, temperature scaling, and logit distillation from a teacher model. This transfers capabilities while reducing model size for faster inference and cost-efficient deployment.

Reverse KLD, or MiniLLM, is a distillation technique that trains smaller student models by minimizing reverse KL divergence. It helps preserve performance when compressing large language models for low-latency inference.

Yes, you can transfer GPT-4 capabilities to open-source backbones through teacher-to-student distillation. The process uses soft targets and multi-teacher strategies to compress large models into compact students for domain adaptation.

Knowledge distillation supports multi-teacher strategies to transfer knowledge from several teacher models to a single student. This approach enhances domain adaptation and multi-domain transfer while maintaining cost-efficient deployment.

Use knowledge distillation for model compression when deploying on edge devices or limited hardware requires low-latency inference. It enables cost-efficient deployment by compressing large language models into smaller, faster student models.

The best way to compress a 70B model to 7B is teacher-to-student distillation with reverse KLD and temperature scaling. This achieves minimal performance loss while enabling practical deployment on defined tasks.