WarpQuant Llama 3 8B R16E4H4
Llama 3 8B quantized with signed Hadamard rotation, block-GPTQ, and Output-Fisher weak-column recovery. Projection weights use a 3.5-bpw INT3 base, selected columns are restored in BF16, and the embedding and output head use group-128 INT4.
Payload and evaluation
| Format | Text bpw | Payload | WikiText-2 PPL ↓ | ARC-299 ↑ | MMLU-13,943 ↑ |
|---|---|---|---|---|---|
| BF16 | 16.00 | 14.965 GiB | 6.2559 | 50.50 | 41.04 |
| Q4_K_M | 4.89 | 4.583 GiB | 6.4359 | 50.84 | 40.67 |
| IQ3_S + imatrix | 3.66 | 3.429 GiB | 6.9929 | 44.15 | 39.87 |
| WarpQuant Fisher R16E4H4 | 3.6256 | 3.389 GiB | 7.3446 | 45.49 | 38.99 |
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
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "HarimxChoi/WarpQuant-Llama-3-8B-R16E4H4"
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/}
}