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gptq SKILL.md skillA

This file is byte-identical to the first copy the registry indexed, one of 3 indexed copies. Same content hash, so the same checks give both the same grade.

gptq 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/gptq@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: 10-optimization/gptq/SKILL.md
Versions recorded: 5
Latest version: v1.0.0, committed Nov 20, 2025
First version recorded: Nov 9, 2025
Size: 11,593 bytes
Repository stars: 13,718
License: MIT

Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.

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

# GPTQ (Generative Pre-trained Transformer Quantization)

Post-training quantization method that compresses LLMs to 4-bit with minimal accuracy loss using group-wise quantization.

## When to use GPTQ

**Use GPTQ when:**
- Need to fit large models (70B+) on limited GPU memory
- Want 4× memory reduction with <2% accuracy loss
- Deploying on consumer GPUs (RTX 4090, 3090)
- Need faster inference (3-4× speedup vs FP16)

**Use AWQ instead when:**
- Need slightly better accuracy (<1% loss)
- Have newer GPUs (Ampere, Ada)
- Want Marlin kernel support (2× faster on some GPUs)

**Use bitsandbytes instead when:**
- Need simple integration with transformers
- Want 8-bit quantization (less compression, better quality)
- Don't need pre-quantized model files

## Quick start

### Installation

```bash
# Install AutoGPTQ
pip install auto-gptq

# With Triton (Linux only, faster)
pip install auto-gptq[triton]

# With CUDA extensions (faster)
pip install auto-gptq --no-build-isolation

# Full installation
pip install auto-gptq transformers accelerate
```

### Load pre-quantized model

```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint

mdr add orchestra-research/ai-research-skills/gptq@sha256:ce19de416afa03a0

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_uthrbkcv6scx732z.svg)](https://markdownregistry.com/a/art_uthrbkcv6scx732z)

1 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 5 versions of this file, between Nov 9, 2025 and Nov 20, 2025, about one every 3 days.

version	committed	commit	size	audit
v1.0.0 latest	2025-11-20	0a9ded4	11,593 B	A	view · diff
v1.0.0	2025-11-20	51fc3a3	11,579 B	A	view · diff
v1.0.0	2025-11-20	81c38fe	11,578 B	A	view · diff
v1.0.0	2025-11-19	bd4461f	11,577 B	A	view · diff
git:20251109.096b8f0	2025-11-09	096b8f0	11,336 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 (11593 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_uthrbkcv6scx732z
GET https://markdownregistry.com/api/v1/resolve?ref=orchestra-research/ai-research-skills/gptq
GET https://markdownregistry.com/api/v1/blob/ce19de416afa03a0e6a764c9cb8b8ba9a0b94812b9de1ad5a0c2cb2932009f28

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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