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knowledge-distillation

Compress Llama-2-70b to Llama-2-7b knowledge via softmax temperature distillation.

Updated Aug 23, 2026
Search Tags:#llm#knowledge distillation#model compression#model transfer#teacher-student#reverse kld
One-click install
npx skills add https://github.com/choice5346/BiSHE --skill knowledge-distillation-choice5346
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: knowledge-distillation
Source: https://github.com/choice5346/BiSHE/tree/main/.github/skills/knowledge-distillation
Command: npx skills add https://github.com/choice5346/BiSHE --skill knowledge-distillation-choice5346

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of deploying large, computationally expensive language models by enabling the creation of smaller, more efficient student models that retain a significant portion of the teacher model's performance.

Core Features & Use Cases

  • Model Compression: Reduce model size (e.g., 70B to 7B parameters) while preserving high accuracy (90%+).
  • Capability Transfer: Distill advanced capabilities from proprietary models (like GPT-4) into open-source alternatives (LLaMA, Mistral).
  • Cost Reduction: Lower inference costs by using smaller, faster student models.
  • Specialized Models: Create domain-specific models by distilling targeted knowledge.
  • Use Case: You need to deploy a powerful language model on edge devices with limited resources. Use this Skill to distill the knowledge from a large, cloud-based model into a compact version suitable for on-device inference.

Quick Start

Use the knowledge-distillation skill to compress the Llama-2-70b model into a Llama-2-7b model using temperature scaling and soft targets.

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 for edge device deployment?▼

Compress large language models for edge devices by using knowledge distillation to transfer capabilities from a large teacher model into a smaller student model. This reduces inference costs and enables on-device deployment while retaining performance.

What is reverse KLD in knowledge distillation?▼

Reverse KLD is a supported distillation method that transfers knowledge from teacher to student models. It works alongside temperature scaling, soft targets, and logit distillation to ensure the student model retains high accuracy during compression.

Can I distill capabilities from GPT-4 into open-source models like LLaMA or Mistral?▼

You can distill capabilities from proprietary models into open-source alternatives like LLaMA and Mistral. This capability transfer creates specialized or compact models that lower inference costs while preserving advanced performance.

Does knowledge distillation work with PyTorch and DeepSpeed?▼

Knowledge distillation works directly with PyTorch, DeepSpeed, Transformers, and Datasets. These libraries provide the necessary environment to execute teacher-student training and logit distillation workflows.

How much performance is retained when compressing a 70B model to a 7B model?▼

Compressing a 70B model to a 7B model retains over 90% accuracy when using soft targets and temperature scaling. This significant parameter reduction lowers inference costs while preserving the teacher model's capabilities.

What are the limitations of using logit distillation for model compression?▼

Logit distillation requires both teacher and student models to share the same vocabulary size to align outputs. This constraint limits model compression pairing options when transferring knowledge across different architectures.

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