Naming notice (2026-04-10). The "PolarQuant" technique used in this model is being rebranded to HLWQ (Hadamard-Lloyd Weight Quantization). The change is only the name; the algorithm and the weights in this repository are unchanged.

The rebrand resolves a name collision with an unrelated, earlier KV cache quantization method also named PolarQuant (Han et al., arXiv:2502.02617, 2025). HLWQ addresses weight quantization with a deterministic Walsh-Hadamard rotation and Lloyd-Max scalar codebook; Han et al.'s PolarQuant addresses KV cache quantization with a random polar rotation. The two methods are technically distinct.

Existing loaders that load this repository by ID continue to work without changes. Future model uploads will use the HLWQ name.

Reference paper for this technique: arXiv:2603.29078 (v2 in preparation; v1 still uses the old name).

🧊 Gemma-4-31B-Claude-Opus-PolarQuant-Q5-Vision

Claude Opus distilled Gemma 4 31B + Vision on consumer GPUs.

Download: 21.8 GB (vs 62.5 GB BF16 — 2.9x compression)

Component	Method	Result
Text weights	PolarQuant Q5 + torchao INT4	21.8 GB
Vision encoder	BF16 (full quality)	included
KV Cache	PolarQuant Q3 (5.3x)	longer context
Reasoning	Claude Opus 4.6 distilled	high-effort

🎯 Key Results

Metric	Value
VRAM	22.8 GB (streaming loader)
Speed	~24.9 tok/s
Download	21.8 GB
Vision	✅ Golden Gate Bridge
Compression	2.9x
Quantized layers	602

📊 Charts

🏆 GPU Support

GPU	VRAM	Fits?
RTX 4090	24 GB	✅
L4	24 GB	✅
RTX 5090	32 GB	✅
A100	40-80 GB	✅

🚀 Quick Start

pip install polarquant[all]
polarquant chat TeichAI/gemma-4-31B-it-Claude-Opus-Distill --vision

🔬 KV Cache Compression

Method	Bits	Compression	Max Context (4GB)
FP16	16	1.0x	4K
PolarQuant Q4	4	4.0x	17K
PolarQuant Q3	3	5.3x	22K
PolarQuant Q2	2	8.0x	35K

🔧 Technical Details

• Architecture: Gemma 4 (60 layers, 32 attn heads, 16 KV heads, head_dim=256)
• Hybrid attention: Sliding window (1024) + global attention
• Weight quantization: Hadamard rotation (128x128) + Lloyd-Max Q5 + torchao INT4
• KV cache: Hadamard rotation (256x256) + Lloyd-Max Q3 + real bit-packing
• Streaming loader: Per-module INT4 via nn.Sequential wrapper — fits 24GB GPUs
• Base model: TeichAI/gemma-4-31B-it-Claude-Opus-Distill

📖 Citation

@article{polarquant2025,
  title={PolarQuant: Hadamard-Rotated Lloyd-Max Quantization for LLM Compression},
  author={Vicentino, Caio},
  journal={arXiv preprint arXiv:2603.29078},
  year={2025}
}

📄 Paper · 💻 GitHub · 📦 PyPI

---

🚀 Quick Start

Install

pip install git+https://github.com/caiovicentino/polarengine-vllm.git

Load & Generate (1 line!)

from polarengine_vllm import PolarQuantModel

model = PolarQuantModel.from_pretrained("caiovicentino1/Gemma-4-31B-Claude-Opus-PolarQuant-Q5-Vision")
print(model.generate("Hello, how are you?", max_new_tokens=100))

With KV Cache Compression (5.3x more context)

model = PolarQuantModel.from_pretrained("caiovicentino1/Gemma-4-31B-Claude-Opus-PolarQuant-Q5-Vision", kv_cache_nbits=3)
# KV cache now uses 5.3x less memory — fit longer conversations!
print(model.generate("Explain quantum computing in detail.", max_new_tokens=500))

Benchmark

polarquant bench caiovicentino1/Gemma-4-31B-Claude-Opus-PolarQuant-Q5-Vision --ppl --chart

Gradio Demo

polarquant demo caiovicentino1/Gemma-4-31B-Claude-Opus-PolarQuant-Q5-Vision --share

📦 Method: PolarQuant

Hadamard Rotation + Lloyd-Max Optimal Centroids

Unlike GGUF (uniform quantization), PolarQuant places quantization levels where weight density is highest — mathematically proven optimal for Gaussian-distributed neural network weights.

PolarQuant Q5 (cos_sim > 0.996) > GGUF Q5_K_M (~0.99) at same size

🔗 Links

• 📄 Paper — arXiv:2603.29078
• 💻 GitHub — PolarEngine
• 📦 PyPI — pip install polarquant