kvq 0.1.0

Norm-Aware KVQuant: Precision Where It Counts

Norm-Aware KV Cache Quantization

• Quantize What Counts: Bit Allocation Insights Informed by Spectral Gaps in Keys and Values.

• Norm-Aware KVQuant: Precision Where It Counts

Installation

To install the package from PyPI, run the following command:

pip install kvq

Usage

1. Initialization

   1.1. Creating a KVQ object using a configuration object:

   import torch
   from kvq import KVQConfig, KVQ
   
   
   config = KVQConfig(
       budget = 4,
       model="meta-llama/Llama-3.1-8B-Instruct"
       residual_length=32,
       group_size={"k": 64, "v": 64}, # Group size for keys and values
       axis={"k": 0, "v": 0}, # Axis along which to quantize
   )
   
   kv_cache = KVQ(config)
   
   text = "What is the meaning of life?"
   
   inputs = tokenizer(text, return_tensors="pt").to(model.device)
   
   outputs = model.generate(
       **inputs,
       max_new_tokens=256,
       past_key_values=kv_cache,
       use_cache=True,
       pad_token_id=tokenizer.eos_token_id,
   )

GitHub Repository

The source code is hosted on GitHub:

https://github.com/mohsenhariri/spectral-kv

Feel free to open issues, suggest improvements, or submit pull requests!

Citation

If you find our work useful or interesting, please consider citing our paper:

@article{hariri2025quantize,
title     = {Quantize What Counts: Bit Allocation Insights Informed by Spectral Gaps in Keys and Values},
author    = {Hariri, Mohsen and Luo, Alan and Nemati, Mohammadreza and Nguyen, Lam and Zhong, Shaochen and Wang, Qifan and Hu, Xia and Han, Xiaotian and Chaudhary, Vipin},
journal   = {arXiv preprint arXiv:2502.15075},
year      = {2025},
url       = {https://arxiv.org/abs/2502.15075v2},
}