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Fine-tuningFreeOpen Source
AUTOROUND
Intel's advanced LLM weight quantization library
Apache-2.0
ABOUT
Deploying large language models at scale requires significant GPU memory and compute, and naive quantization often degrades model quality significantly — especially at aggressive bit-widths like 4-bit and 3-bit. AutoRound solves this by using sign-optimized rounding to find the optimal weight rounding direction for each parameter, achieving substantially better accuracy than standard round-to-nearest quantization across model families and bit-widths.
INSTALL
pip install auto-roundINTEGRATION GUIDE
1. Model compression: quantize LLMs to 4-bit, 3-bit, or even 2-bit precision while preserving accuracy through optimized weight rounding
2. Production deployment: reduce GPU memory requirements for serving LLMs by 4x or more without sacrificing output quality
3. Accuracy-aware quantization: automatically compare quantization strategies and select the optimal rounding for each layer to minimize perplexity degradation
4. Multi-model support: quantize LLaMA, Mistral, Qwen, Falcon, and other popular model families with consistent quality results
5. Custom bit-widths: experiment with non-standard quantization configurations including INT4, FP4, INT3, and mixed-precision schemes
TAGS
quantizationmodel-compressionint4optimizationllmweightsinference-optimization