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knowledge-distillation

Compress large language models by distilling knowledge from teacher to student models.

6|3|Updated Jan 29, 2026
Search Tags:#LLM#knowledge distillation#temperature scaling#model compression#soft targets#MiniLLM#model transfer#reverse KLD#logit distillation
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill knowledge-distillation-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: knowledge-distillation
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/knowledge-distillation
Command: npx skills add https://github.com/jonnabio/ace-framework --skill knowledge-distillation-jonnabio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, torch, datasets, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of compressing large language models (LLMs) while retaining high performance, enabling the deployment of smaller, more efficient models.

Core Features & Use Cases

  • Model Compression: Reduce the size of LLMs by distilling knowledge from larger teacher models to smaller student models.
  • Performance Retention: Maintain 90%+ of the original model's performance after compression.
  • Use Case: Transfer the capabilities of a 70B parameter GPT-4 model to a 7B parameter LLaMA model, achieving near parity in performance while reducing inference costs.

Quick Start

Train a knowledge-distilled model using the knowledge-distillation skill with the teacher model 'meta-llama/Llama-2-70b-hf' and the student model 'meta-llama/Llama-2-7b-hf'.

Frequently Asked Questions about knowledge-distillation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compress a large language model using knowledge distillation?▼

Knowledge distillation compresses large language models by transferring capabilities from a larger teacher model to a smaller student model, reducing inference costs while retaining 90%+ of original performance.

Can I transfer capabilities from a 70B parameter model to a 7B model with PyTorch?▼

Yes, you can transfer capabilities from a 70B parameter model to a 7B model using PyTorch, achieving near parity in performance while significantly reducing inference costs.

What libraries do I need to set up model compression with soft targets?▼

To set up model compression with soft targets, you need PyTorch, Transformers, and Datasets libraries to facilitate the knowledge distillation process from teacher to student models.

Does knowledge distillation maintain performance when reducing LLM size?▼

Knowledge distillation maintains 90%+ of the original model's performance when reducing LLM size by using techniques like temperature scaling and reverse KLD to train the student model.

When should I use logit distillation for model compression?▼

Use logit distillation for model compression when you need to deploy smaller, efficient models and want to transfer capabilities from proprietary models to open-source alternatives.

What is the best way to reduce LLM inference costs without losing accuracy?▼

The best way to reduce LLM inference costs without losing accuracy is knowledge distillation, which trains a smaller student model using soft targets from a larger teacher model to retain performance.

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