How to benchmark NexusQuant on your own model

Thank you for sharing the detailed guide and benchmark setup instructions for NexusQuant. This is a valuable resource for anyone looking to optimize large language models (LLMs) for production environments by reducing memory usage while maintaining acceptable performance levels.

Here are some key points from your guide:

1. Installation: The package can be installed via pip with pip install nexusquant-kv.

2. Benchmark Setup:

   • A baseline PPL score is calculated without any compression.
   • Compression is applied using different presets (high, balanced, and max).
   • For each preset, the PPL score is recalculated to measure quality degradation.

3. Interpreting Results:

   • If the delta (change in PPL) is less than +1% at the high preset, it indicates that the model can be safely compressed with minimal impact on performance.
   • A significant increase in PPL (+5%) for any preset suggests unusual attention patterns and may warrant further investigation.

4. Preset Recommendations:

   • The balanced preset is generally recommended as it offers a good balance between compression ratio (17x) and quality degradation (~1%).

Example of Benchmark Output

Read the full article at DEV Community

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