gptq
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
- Skills
- AI research
- 0
What it does and how to use it
A skill that teaches Claude Code to do ai research tasks well. It is packaged knowledge the model uses whenever it is relevant.
- Install the skill (command above).
- In Claude Code, describe what you need - the skill turns on by itself when the context fits.
- No commands to memorize: you talk in plain language and the skill does the rest.
Install (Claude Code)
mkdir -p .claude/skills/gptq && curl -fsSL https://doutorai.pt/data/lib/skills__gptq.md -o .claude/skills/gptq/SKILL.mdPaste this command into the terminal, in your project folder, and the component installs automatically.
Content
---
name: gptq
description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Optimization, GPTQ, Quantization, 4-Bit, Post-Training, Memory Optimization, Consumer GPUs, Fast Inference, QLoRA, Group-Wise Quantization]
dependencies: [auto-gptq, transformers, optimum, peft]
---
# G
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