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.
| Format | Text bpw | Payload | WikiText-2 PPL ↓ | ARC-299 ↑ | MMLU-13,943 ↑ |
|---|---|---|---|---|---|
| BF16 | 16.00 | 7.846 GiB | 8.3885 | 45.82 | 39.58 |
| Q4_K_M | 5.13 | 2.523 GiB | 8.5472 | 48.83 | 39.48 |
| IQ3_M | 4.09 | 2.015 GiB | 10.6976 | 42.81 | 37.41 |
| WarpQuant Fisher R16E4 | 3.6514 | 1.788 GiB | 9.2494 | 46.15 | 38.13 |
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/}
}