The Kaitchup – AI on a Budget

The Kaitchup – AI on a Budget

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The Kaitchup – AI on a Budget
The Kaitchup – AI on a Budget
The Impact of the Calibration Dataset for AutoRound and AWQ Quantization

The Impact of the Calibration Dataset for AutoRound and AWQ Quantization

Should you choose the calibration dataset?

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Benjamin Marie
Oct 31, 2024
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The Kaitchup – AI on a Budget
The Kaitchup – AI on a Budget
The Impact of the Calibration Dataset for AutoRound and AWQ Quantization
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Large language models (LLMs) require a lot of memory, making them difficult to run on a GPU, especially larger models or when using consumer GPUs. Quantization can help by compressing LLMs. For example, 4-bit quantization typically reduces an LLM’s size by about one-third.

The most common quantization methods are post-training quantization (PTQ) techniques, like GPTQ, AWQ, and AutoRound.

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These methods are applied to pre-trained models and require a calibration dataset. This dataset measures the quantization error and guides the quantization. The choice of the calibration dataset seems critical to improving the accuracy of the quantization. However, most quantization tools use a general, English-language dataset as the default.

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In this article, we will examine how the choice of calibration dataset affects quantization performance. First, we’ll look at how AWQ and AutoRound leverage the calibration step. Then, we’ll test four different calibration datasets and evaluate quantized models on various benchmarks. We will also experiment with both English and French to see how calibration language impacts results.

The following notebook demonstrates AWQ and AutoRound quantization for Qwen2.5 using different calibration datasets:

Get the notebook (#117)

Calibration for LLM Quantization

The role of the calibration is different for each quantization algorithm. For GPTQ, which is still one of the most popular quantization methods, the key steps are the following:

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