64 lines
1.9 KiB
Markdown
64 lines
1.9 KiB
Markdown
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---
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library_name: transformers
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license: other
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license_name: llama3
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license_link: LICENSE
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base_model: NousResearch/Meta-Llama-3-8B
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pipeline_tag: text-generation
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tags:
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- llama-3
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- text-generation
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- quantization
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- post-training-quantization
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- warpquant
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- hadamard-transform
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- output-fisher
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- pytorch
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- llm
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language:
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- en
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---
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# WarpQuant Llama 3 8B R16E4H4
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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.
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[Technical report](https://harimxchoi.github.io/projects/warpquant/) · [GitHub](https://github.com/HarimxChoi/WarpQuant)
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## Payload and evaluation
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| Format | Text bpw | Payload | WikiText-2 PPL ↓ | ARC-299 ↑ | MMLU-13,943 ↑ |
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|---|---:|---:|---:|---:|---:|
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| BF16 | 16.00 | 14.965 GiB | 6.2559 | 50.50 | 41.04 |
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| Q4_K_M | 4.89 | 4.583 GiB | 6.4359 | 50.84 | 40.67 |
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| IQ3_S + imatrix | 3.66 | 3.429 GiB | 6.9929 | 44.15 | 39.87 |
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| **WarpQuant Fisher R16E4H4** | **3.6256** | **3.389 GiB** | **7.3446** | **45.49** | **38.99** |
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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.
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "HarimxChoi/WarpQuant-Llama-3-8B-R16E4H4"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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```
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## Citation
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```bibtex
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@misc{choi2026warpquant,
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author = {Harim Choi},
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title = {WarpQuant: Dual-Domain LLM Quantization via Hadamard Rotation and Output-Fisher Sensitivity},
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year = {2026},
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url = {https://harimxchoi.github.io/projects/warpquant/}
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}
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```
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