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quantizing-models-bitsandbytes

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.

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Install

armory install quantizing-models-bitsandbytes --cli claude

writes the skill into.claude/skills/quantizing-models-bitsandbytes/SKILL.mdListed as compatible

Configuration
# fetches the source and writes it to:
.claude/skills/quantizing-models-bitsandbytes/SKILL.md

Needs the armory CLI · not on npm yet, build it from cli/ in the repository

What it is

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.

When to use it

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.

How to install / invoke

# Copy the skill into your .claude/skills/ directory
curl -sL https://raw.githubusercontent.com/davila7/claude-code-templates/main/cli-tool/components/skills/ai-research/optimization-bitsandbytes/SKILL.md -o .claude/skills/optimization-bitsandbytes/SKILL.md

Notes

Extracted from davila7/claude-code-templates, optimization-bitsandbytes category.