WarpQuant-Llama-3-8B-R16E4H4

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.

Technical report · GitHub

Payload and evaluation

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/}
}