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.
- Stars
- 14,234
- Version
- v1
- Copies
- 0
Overview
Waxell's checks found 3 findings to review, highest severity high.
- Highest severity
- high
- Findings
- 3
- What ran
- Static analysis
A clean result means nothing alarming in what is published, not a guarantee. How the checks work
We don't republish the skill's instructions. Read them on GitHub at the reviewed commit.
Security
Security scan
Checked 2 days ago
Result
Review findings Waxell's checks found 3 findings to review, highest severity high.
Checked with static analysis: servers it launches, packages it installs at runtime, scripts piped to a shell and bundled binaries. Nothing was run. How the checks work
Findings (3)
high · 3
- high
Tells Claude to install unpinned packages (auto-gptq)
SKILL.md:39 · skill_risk:S2
- high
Tells Claude to install unpinned packages (auto-gptq, transformers, accelerate)
SKILL.md:48 · skill_risk:S2
- high
Tells Claude to install unpinned packages (auto-gptq)
references/troubleshooting.md:14 · skill_risk:S2
Versions
Version history
Each distinct content we have seen, newest first, with the verdict it got at the time.
One version so far: v1 (1.0.0), seen 2026-11-03.
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SOC 2 Type II attested · ISO/IEC 27001:2022 certified · HIPAA with a BAA · aligned to the EU AI Act and NIST AI RMF
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