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DuneBench

dunebench is a lightweight, local benchmarking tool for GGUF models. It allows you to evaluate Large Language Models (LLMs) across a variety of domains—including logic, coding, math, and common sense—using llama-cpp-python.

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Installation

Install dunebench with pip

    pip install dunebench

or install EXE with this link

Features

  • Local Evaluation: Runs entirely on your machine using GGUF models.
  • GPU Accelerated: Offload layers to your GPU for faster testing.
  • Multi-Domain Support: Includes 8 distinct benchmarks (Math, Coding, Science, etc.).

Usage/Examples

python -m dunebench --model "path/to/model.gguf" --task science --limit 20

Arguments

Argument Description Default
--model Path to your .gguf model file Required
--task The benchmark task to run Required
--limit Number of samples to test 10

Tasks

Task Name Dataset Used Domain Type
science ai2_arc (Challenge) Scientific Reasoning Multiple Choice
math gsm8k Math Word Problems Generation
programming mbpp (Sanitized) Python Coding Code Generation
physical_logic piqa Physical Commonsense Multiple Choice
common_sense openbookqa General Knowledge Multiple Choice
logic winogrande Ambiguity Resolution Multiple Choice
grammar glue (CoLA) Linguistic Acceptability Multiple Choice
nlp hellaswag Sentence Completion Multiple Choice

License

MIT

Authors

Metadata

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