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How to use Prism Quant: what you can do with it

What is Prism Quant? PrismQuant is a Python reference implementation of calibrated null-space rotations for grouped INT4 quantization, with released quantized base-model checkpoints.

How do I use it? Run git clone --depth 1 https://github.com/ForeverBlue816/PrismQuant.git and then cd PrismQuant; then run pip install -e . inside the repo folder; run prismquant models to list the released model names. Full steps below.

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What do you want to do?

4 use cases

  • Generate text with a quantized Qwen3 base model

    Run the smallest released Prism Quant checkpoint locally for base-model text completion.

    View prompt
    Clone https://github.com/ForeverBlue816/PrismQuant, install it with pip install -e ., and run prismquant generate --model qwen3_0.6b_base with a prompt of your choice and --max-new-tokens 64. Report the generated text and confirm it is a base-model completion rather than a chat-style answer.

  • Fit a Prism Quant rotation on your own activations

    Use the standalone rotation API on your calibration samples and check the round trip.

    View prompt
    In the PrismQuant repo, write a script that loads <calibration_file> as a torch tensor, calls fit_rotation with rank=2 and group_size=128, quantizes with dynamic_asym_int4, and reconstructs with rotation.transpose. Output the reconstruction error and verify that the tensor shape is unchanged.

  • Check a Prism Quant fork with the release test suite

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

    View prompt
    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.

  • Trace the published WikiText-2 perplexity claim to its sources

    Check the cited score against the paper, catalog revisions and evaluation metadata.

    View prompt
    Using the PrismQuant repo at https://github.com/ForeverBlue816/PrismQuant, read docs/evaluation_metadata.md, docs/reproduction.md and prismquant/models.json. Produce a note listing the Llama-3.1-70B WikiText-2 setting, the pinned revisions, and what the catalog does not verify.

Quick start with Prism Quant

  1. Check you have what it needs: Python 3.10 or newer; A PyTorch installation that matches your CUDA version; Internet access to clone the repo and download Hugging Face checkpoints.
  2. Open ForeverBlue816/PrismQuant on GitHub and read the README and license.
  3. Run git clone --depth 1 https://github.com/ForeverBlue816/PrismQuant.git and then cd PrismQuant.
  4. Run pip install -e . inside the repo folder.
  5. Run prismquant models to list the released model names.
  6. Run prismquant generate --model qwen3_0.6b_base --prompt "The key idea behind quantization is" --max-new-tokens 64. (inferred, check the README)

These steps come from our top use case, Generate text with a quantized Qwen3 base model, which also has a copy-paste agent prompt.

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About this repo

  • Who it is forBuilt for ML engineers and researchers who want to run or study quantized base LLMs and test rotation-based quantization on their own activations.
  • Why it is hot154 stars, launched 54 days ago
  • LanguagePython
  • Sourcegithub.com/ForeverBlue816/PrismQuant · Back to Hot repos

Requirements

  • 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)

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Questions about Prism Quant

What is Prism Quant?

PrismQuant is a Python reference implementation of calibrated null-space rotations for grouped INT4 quantization, with released quantized base-model checkpoints. It is an open-source project on GitHub: https://github.com/ForeverBlue816/PrismQuant.

How do I use Prism Quant?

Run git clone --depth 1 https://github.com/ForeverBlue816/PrismQuant.git and then cd PrismQuant; then run pip install -e . inside the repo folder; run prismquant models to list the released model names. The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is Prism Quant for?

Built for ML engineers and researchers who want to run or study quantized base LLMs and test rotation-based quantization on their own activations.

What can I do with Prism Quant?

4 use cases so far, for example: Generate text with a quantized Qwen3 base model; Fit a Prism Quant rotation on your own activations; Check a Prism Quant fork with the release test suite. Each has steps and a copy-paste agent prompt.

What do I need to run Prism Quant?

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).

What are alternatives to Prism Quant?

Other hot AI repos in Software and DevOps: Deep Infer, Harness2, Orca, Opencode, Graphify.

Is Prism Quant safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.