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How to check a Prism Quant fork with the release test suite with Prism Quant

Install the dev extras and run pytest to confirm algebra, loading and generation checks pass.

Short answer: Clone the repo with git clone --depth 1 https://github.com/ForeverBlue816/PrismQuant.git and enter the folder with cd PrismQuant; then install the development extras with pip install -e '.[dev]'; run pytest -q and note any failing tests. All steps.

Who it is forA maintainer who forked Prism Quant or changed its quantization code and needs evidence the core behavior still holds.

The problemAfter editing rotation or quantization code, it is unclear whether the numerical properties the project relies on still hold.

The outcomeA clean pytest run covering the repo's algebra, metadata, loading and generation checks, with the scope documented in docs/validation.md.

What you need first

  • Python 3.10 or newer
  • A PyTorch installation that matches your CUDA version
  • Internet access to clone the repo and download Hugging Face checkpoints
  • Enough GPU or system memory for the chosen model size (see docs/models.md)

Steps

  1. Clone the repo with git clone --depth 1 https://github.com/ForeverBlue816/PrismQuant.git and enter the folder with cd PrismQuant.
  2. Install the development extras with pip install -e '.[dev]'.
  3. Run pytest -q and note any failing tests.
  4. Open docs/validation.md to see which numerical checks the tests cover and which they do not.
  5. Record which of your changes the passing tests do not cover, and add tests for those areas.

Prompt for your agent

Clone https://github.com/ForeverBlue816/PrismQuant, run pip install -e '.[dev]' and then pytest -q. Report the pass and fail counts, then read docs/validation.md and list the areas of <changed_module> that the tests do not cover.

Caveat. Passing the release tests shows the packaged checks hold, not that a modified kernel or new model is accurate on the paper's benchmarks.

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