---
title: "How should I test whether quantizing a DeepSeek model hurts exact identifier copying?"  
description: "How should I test whether quantizing a DeepSeek model hurts exact identifier copying?"  
author: "Otto Lehtonen"  
published: 2026-10-18  
updated: 2026-10-18  
canonical: https://www.mindstick.com/forum/162560/how-should-i-test-whether-quantizing-a-deepseek-model-hurts-exact-identifier-copying  
category: "Local Model Evaluation"  
tags: ["DeepSeek", "Model Quantization", "LLM Evaluation"]  
reading_time: 1 minute  

---

# How should I test whether quantizing a DeepSeek model hurts exact identifier copying?

I’m choosing between quantized builds of the same DeepSeek model for a local support tool. Fluent explanations matter, but the less forgiving requirement is copying product identifiers exactly from supplied text.

For example, `AX-1048-B` must not become `AX-1084-B`. An answer can read perfectly and still send someone to the wrong replacement part.

### The comparison I’m planning

I would keep the source prompts, tokenizer, chat template, context limit, and decoding settings consistent, then compare a higher-precision reference with lower-bit builds. The test set would include similar-looking identifiers, leading zeros, and several product codes in the same passage.

For **exact identifier copying**, should I score only character-for-character matches, or also separate wrong selection from transcription errors? Choosing the wrong code from the passage seems different from inventing a code that was never there.

What else should a **quantized model accuracy benchmark** control for before I attribute a regression to quantization? I’m looking for a practical evaluation, not just a general language benchmark score.


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Original Source: https://www.mindstick.com/forum/162560/how-should-i-test-whether-quantizing-a-deepseek-model-hurts-exact-identifier-copying

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