WarpQuant-Qwen3.5-4B-R16E4-Text

WarpQuant Qwen3.5-4B R16E4 Text

Text-only export of the Qwen3.5-4B WarpQuant checkpoint. Projection weights use a signed-Hadamard block-GPTQ INT3 base, Output-Fisher selects the BF16 recovery columns, and the token embedding uses group-128 INT4.

Technical report · GitHub · VLM model

Payload and evaluation

The denominator is the 4,205,751,296-parameter text model; vision tensors are excluded.

The repository stores the quantized values in BF16-compatible safetensors. The reported payload is the packed-equivalent analytical size including codes, scales, recovery values, and column indices.

Usage

Qwen3.5 currently requires the latest Transformers main branch:

pip install "transformers @ git+https://github.com/huggingface/transformers.git@main"

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "HarimxChoi/WarpQuant-Qwen3.5-4B-R16E4-Text"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

Citation

@misc{choi2026warpquant,
  author = {Harim Choi},
  title = {WarpQuant: Dual-Domain LLM Quantization via Hadamard Rotation and Output-Fisher Sensitivity},
  year = {2026},
  url = {https://harimxchoi.github.io/projects/warpquant/}
}