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knowledge-distillation SKILL.md skillA

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knowledge-distillation from orchestra-research/ai-research-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned

mdr add orchestra-research/ai-research-skills/knowledge-distillation@v1.0.0

Pins v1.0.0 in mdr.lock. Pin the fingerprint instead.

Audit grade: A, 17 of 17 checks passed
Kind: SKILL.md skill
Repository: orchestra-research/ai-research-skills
Path: 19-emerging-techniques/knowledge-distillation/SKILL.md
Versions recorded: 5
Latest version: v1.0.0, committed Nov 20, 2025
First version recorded: Nov 19, 2025
Size: 13,600 bytes
Repository stars: 13,718
License: MIT

Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# Knowledge Distillation: Compressing LLMs

## When to Use This Skill

Use Knowledge Distillation when you need to:
- **Compress models** from 70B → 7B while retaining 90%+ performance
- **Transfer capabilities** from proprietary models (GPT-4) to open-source (LLaMA, Mistral)
- **Reduce inference costs** by deploying smaller student models
- **Create specialized models** by distilling domain-specific knowledge
- **Improve small models** using synthetic data from large teachers

**Key Techniques**: Temperature scaling, soft targets, reverse KLD (MiniLLM), logit distillation, response distillation

**Papers**: Hinton et al. 2015 (arXiv 1503.02531), MiniLLM (arXiv 2306.08543), KD Survey (arXiv 2402.13116)

## Installation

```bash
# Standard transformers
pip install transformers datasets accelerate

# For training
pip install torch deepspeed wandb

# Optional: MiniLLM implementation
git clone https://github.com/microsoft/LMOps
cd LMOps/minillm
pip install -e .
```

## Quick Start

### Basic Knowledge Distillation

```python
import torch
import torch.nn.functional as F
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint

mdr add orchestra-research/ai-research-skills/knowledge-distillation@sha256:bc27086dc02840ce

The line at the top of this page pins the label v1.0.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

[![mdr](https://markdownregistry.com/badge/art_kmt36pvreiiex7r6.svg)](https://markdownregistry.com/a/art_kmt36pvreiiex7r6)

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Versions

The registry has recorded 5 versions of this file, between Nov 19, 2025 and Nov 20, 2025, more than one a day.

version	committed	commit	size	audit
v1.0.0 latest	2025-11-20	0a9ded4	13,600 B	A	view · diff
v1.0.0	2025-11-20	51fc3a3	13,579 B	A	view · diff
v1.0.0	2025-11-20	81c38fe	13,578 B	A	view · diff
v1.0.0	2025-11-19	bd4461f	13,577 B	A	view · diff
git:20251119.66e31d2	2025-11-19	66e31d2	13,316 B	A	view

Audit of the latest version

A 17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.

pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (13600 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag

Source

GitHub

orchestra-research/ai-research-skills · 13,718 stars · license MIT · pushed 2026-10-11 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_kmt36pvreiiex7r6
GET https://markdownregistry.com/api/v1/resolve?ref=orchestra-research/ai-research-skills/knowledge-distillation
GET https://markdownregistry.com/api/v1/blob/bc27086dc02840cebef4db34db4b8406b37ff349d6e2a52848646c8daf24ff3e

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.

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