knowledge-distillation

Knowledge distillation compresses LLMs by transferring capabilities from large teacher models to smaller student models. It uses temperature scaling and soft targets to preserve performance while reducing inference costs.

Distilling GPT-4 capabilities into open-source models is achieved through cross-domain transfer using a multi-strategy training pipeline. This applies soft targets and logit distillation to create specialized, domain-specific variants.

Reverse KLD (MiniLLM) is applied in logit distillation to improve generation quality and diversity. You use it within a multi-strategy training pipeline that mixes soft and hard losses to transfer teacher distributions to the student model.

Knowledge distillation supports temperature scaling to soften teacher distributions, generating richer soft targets. This technique is combined with reverse KLD and logit distillation to effectively train the student model.

You can create domain-specific variants using a teacher-student model compression pipeline by performing cross-domain transfer. This distills capabilities from large teacher LLMs into smaller, specialized student models for targeted use cases.