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A Coding Implementation to Compress and Benchmark Instruction-Tuned LLMs with FP8, GPTQ, and SmoothQuant Quantization using llmcompressor

This tutorial demonstrates how to apply post-training quantization to an instruction-tuned language model using the llmcompressor library. It compares FP8 dynamic quantization, GPTQ W4A16, and SmoothQuant with GPTQ W8A8, benchmarking each for disk size, latency, throughput, perplexity, and output quality. The article provides reusable Colab-ready code and insights into compression trade-offs.

SourceMarkTechPostAuthor: Sana Hassan
A Coding Implementation to Compress and Benchmark Instruction-Tuned LLMs with FP8, GPTQ, and SmoothQuant Quantization using llmcompressor
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Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.

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Key points

  • Post-training quantization can significantly reduce model size and improve inference speed.
  • FP8 dynamic quantization offers a fast, data-free compression method with minimal quality loss.
  • GPTQ-based methods require calibration data but achieve higher compression ratios with better accuracy recovery.
  • SmoothQuant combined with GPTQ W8A8 handles activation outliers for robust 8-bit quantization.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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