Which libraries are required for this skill?

You will need Python with the transformers, torch, and datasets libraries installed; additional tools like DeepSpeed are recommended for training.

Can I use this to distill GPT-4 into a local model?

Yes, the skill provides patterns for response distillation, allowing you to use synthetic data from proprietary models to fine-tune open-source student models.

What is the main benefit of using Knowledge Distillation?

It allows you to create smaller, faster, and cheaper AI models that perform nearly as well as much larger, more expensive models by learning from their probability distributions.

What temperature should I use for distillation?

The skill suggests a temperature (T) between 2.0 and 5.0 to soften probability distributions, with 2.0 being the most common default for balanced knowledge transfer.

Does this skill support MiniLLM techniques?

Yes, it includes implementation details for Reverse KLD, which is the core innovation behind MiniLLM for better generative model distillation.

Knowledge Distillation for LLMs

Name: Knowledge Distillation for LLMs
Author: zechenzhangAGI

byzechenzhangAGI

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384

Ciencia de Datos y ML

Compresses large language models using teacher-student learning techniques to reduce inference costs while maintaining high performance.

Acerca de

This skill provides comprehensive guidance and implementation patterns for Knowledge Distillation (KD), enabling developers to transfer intelligence from large 'teacher' models like GPT-4 to smaller, more efficient 'student' models like Llama or Mistral. It covers advanced techniques including temperature scaling, soft targets, and MiniLLM-style reverse KLD to optimize model size without sacrificing significant accuracy. This is a critical tool for AI researchers and engineers looking to deploy high-performance models in resource-constrained environments or significantly reduce cloud compute expenses.

Características Principales

Multi-teacher ensemble distillation workflows
Advanced Reverse KLD (MiniLLM) strategies for generative model optimization
Logit-based and response-based distillation patterns for diverse training scenarios
Implementation of soft targets and temperature scaling for probability softening
Integration-ready code for Transformers, PyTorch, and DeepSpeed libraries
384 GitHub stars

Casos de Uso

Transferring proprietary model capabilities to private, open-source architectures
Optimizing domain-specific models for faster, lower-cost production inference
Compressing 70B parameter models to 7B while retaining over 90% performance

Acerca de

Características Principales

Multi-teacher ensemble distillation workflows
Advanced Reverse KLD (MiniLLM) strategies for generative model optimization
Logit-based and response-based distillation patterns for diverse training scenarios
Implementation of soft targets and temperature scaling for probability softening
Integration-ready code for Transformers, PyTorch, and DeepSpeed libraries
384 GitHub stars

Casos de Uso

Transferring proprietary model capabilities to private, open-source architectures
Optimizing domain-specific models for faster, lower-cost production inference
Compressing 70B parameter models to 7B while retaining over 90% performance