knowledge-distillation

What problem does it solve?

This Skill addresses the challenge of deploying large language models by enabling their compression into smaller, more efficient student models without significant performance degradation.

Core Features & Use Cases

• Model Compression: Reduce model size (e.g., 70B to 7B parameters) while preserving over 90% of the original performance.
• Knowledge Transfer: Transfer capabilities from proprietary models (like GPT-4) to open-source alternatives.
• Cost Reduction: Lower inference costs by using smaller, more manageable student models.
• Use Case: Distill the knowledge of a large, expensive-to-run teacher model into a smaller, faster student model for deployment on edge devices or in resource-constrained environments.

Quick Start

Use the knowledge-distillation skill to distill the Llama-2-70b-hf model into the Llama-2-7b-hf model using a temperature of 2.0 and an alpha of 0.7.