---
library_name: transformers
license: other
license_name: llama3
license_link: LICENSE
base_model: NousResearch/Meta-Llama-3-8B
pipeline_tag: text-generation
tags:
  - llama-3
  - text-generation
  - quantization
  - post-training-quantization
  - warpquant
  - hadamard-transform
  - output-fisher
  - pytorch
  - llm
language:
  - en
---

# 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](https://harimxchoi.github.io/projects/warpquant/) · [GitHub](https://github.com/HarimxChoi/WarpQuant)

## 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

```python
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

```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/}
}
```