---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3.5-4B
pipeline_tag: text-generation
tags:
  - qwen3.5
  - text-generation
  - quantization
  - post-training-quantization
  - warpquant
  - hadamard-transform
  - output-fisher
  - pytorch
  - llm
language:
  - en
  - ko
---

# 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](https://harimxchoi.github.io/projects/warpquant/) · [GitHub](https://github.com/HarimxChoi/WarpQuant) · [VLM model](https://huggingface.co/HarimxChoi/WarpQuant-Qwen3.5-4B-R16E4V4)

## 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:

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

```python
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

```bibtex
@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/}
}
```