ginkgoq-turboquant 0.1.0.post1

TurboQuant implementation

TorbuQuant

LLM KV-cache compression, vector quantization, cache accounting, and serving-path experiments.

A GinkgoQ research implementation for measuring when compressed KV storage helps real long-context inference workloads.

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Purpose

TorbuQuant is a Python package for studying and implementing TurboQuant-style LLM KV-cache compression. The repository keeps the research path and serving path separate:

Path	Role
Diagnostic path	Reconstructs or compares dense tensors so logits, attention outputs, and generation behavior can be inspected.
Serving path	Stores historical K/V in packed byte layouts and measures whether that storage can improve capacity under a memory bound.

The project is built around three measurable goals:

1. Reduce KV-cache storage bytes while counting metadata and workspace.
2. Preserve model behavior under explicit quality gates.
3. Improve practical serving capacity when KV memory, context length, or batch size is the bottleneck.

What Is In This Repository

Area	Status
Codebooks	Exact Beta-sphere Lloyd-Max codebooks and Gaussian approximation.
Rotation	QR and RHT modes.
Vector quantization	MSE direction quantization with packed index storage.
QJL	Residual sign projection for vector and inner-product experiments.
KV formats	K16, K8, K4 and V8, V4, V3, V2 helpers.
Cache accounting	Packed bytes, norms, scales, zeros, metadata, recent windows, and workspace reports.
Attention	Dense, dequantized, direct-QK, packed-V, and selected Triton decode paths.
vLLM integration	Metadata, page geometry, page cache, registry, runtime selection, and verification helpers.
HuggingFace integration	Diagnostic DynamicCache wrapper and Qwen evaluation scripts.
Qwen A/B	Controlled baseline-vs-TurboQuant evaluation script with isolated run directories.

Install

Create and activate a Python environment, then install the repository from the project root:

Install from source

python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e .

Install from PyPI

python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install ginkgoq-turboquant

Quick Checks

Run the repository test suite:

python -m pytest tests -q

Build the documentation site:

python -m mkdocs build --strict

Run the Qwen A/B evaluator with explicit scenario settings before making any memory, quality, or speed statement:

python scripts/qwen_ab_eval.py --help

Repository Map

turboquant/
  core/          codebooks, rotations, MSE quantization, QJL
  packing/       bit packing and byte accounting helpers
  kv/            compressed cache formats and memory reports
  attention/     dense, diagnostic, and packed-reference attention paths
  triton/        update/decode kernels and reference wrappers
  integration/   HuggingFace and vLLM integration layers
  quality/       comparison, retrieval, and generation metrics
scripts/         metadata, verification, benchmark, and Qwen A/B entry points
docs/            MkDocs Material publication site
tests/           unit and integration tests