Individual skill
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 FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.
backend/cli/skills/ml-training/gptq/SKILL.md
Source commit a08e0ad0ad3d6448123039ee4659163eb2e35d99
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 2, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 4,353
- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent Repository topic
- ai Repository topic
- bun Repository topic
- cli Repository topic
- llm Repository topic
- ml Repository topic
- open-source Repository topic
- research Repository topic
- science Repository topic
- ai-agent Repository topic
- optimization Declared skill tag
- gptq Declared skill tag
- quantization Declared skill tag
- 4-bit Declared skill tag
- post-training Declared skill tag
- memory-optimization Declared skill tag
- consumer-gpus Declared skill tag
- fast-inference Declared skill tag
- qlora Declared skill tag
- group-wise-quantization Declared skill tag
- engineering Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesSKILL.mdcore · 12,111 bytesreferences/calibration.mddoc · 8,164 bytesreferences/integration.mddoc · 2,797 bytesreferences/troubleshooting.mddoc · 1,899 bytes
Read excerpt
# 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)