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 bitsandbytes?
firecrawl/ai-research-skills/bitsandbytes
$npx -y skills add firecrawl/ai-research-skills --skill bitsandbytesInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/firecrawl/ai-research-skills" --skill "firecrawl/ai-research-skills/bitsandbytes"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/firecrawl/ai-research-skills" that are relevant to the current task. Run `npx skills add "https://github.com/firecrawl/ai-research-skills"` and select the relevant skills, then follow their instructions.
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