Instructions to use caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5")
model = AutoModelForImageTextToText.from_pretrained("caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5

SGLang

How to use caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5 with Docker Model Runner:
```
docker model run hf.co/caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5
```

⚡ Qwen3.6-35B-A3B — HLWQ Q5

Hadamard-Lloyd Weight Quantization of Qwen/Qwen3.6-35B-A3B

🔬 First HLWQ quantization of a 256-expert hybrid GDN+Attention MoE model

📊 Compression Pipeline

Metric	Value
🎯 Weight bits	5 (HLWQ Q5 — Lloyd-Max + Hadamard)
📦 polar_state	21.55 GB (6 shards, 62,190 keys)
🔢 Coverage	95.8% of 35.11B params (33.62B quantized)
⏱️ Quantization time	60s (PQ5) + 65s (CT INT4)
🏗️ Architecture	40L hybrid (30 GDN + 10 Full Attention)
🧩 Experts	256/layer, 8 routed + 1 shared

🏎️ Speed Benchmarks

RTX PRO 6000 Blackwell (96 GB). FP16 KV uses optimized model.generate(); Q3/Q2 KV use manual generation loop with PolarQuantKVCache.

🧬 Architecture

Component	Spec
Hidden dim	2048
Head dim	256 (full attn) / 128 (GDN)
Expert intermediate	512
Vocab	248,320
Context	262,144 tokens
Vision	27-layer ViT (kept BF16)

📋 Quantization Coverage

Component	Count	Status
MoE expert slices	20,480	✅ HLWQ Q5
Attention projections	130	✅ HLWQ Q5
Shared expert MLPs	120	✅ HLWQ Q5
Norms, layernorms	—	⬜ BF16
MoE routers	40	⬜ BF16
GDN gates (in_proj_a/b)	60	⬜ BF16 (critical)
Vision encoder	27 layers	⬜ BF16
MTP layer	1	⬜ BF16

🚀 Deployment

For inference, use the CT INT4 version:

👉 caiovicentino1/Qwen3.6-35B-A3B-HLWQ-CT-INT4

pip install git+https://github.com/caiovicentino/vllm-expert-offload.git
vllm serve caiovicentino1/Qwen3.6-35B-A3B-HLWQ-CT-INT4 \
  --language-model-only --enforce-eager --moe-expert-cache-size 8

GPU	Expert Cache	VRAM
RTX PRO 6000 (96 GB)	all-in	~20 GB
RTX 4090 (24 GB)	cache=4	~4 GB
RTX 3060 (12 GB)	cache=2	~3 GB

🔬 Method

HLWQ (Hadamard-Lloyd Weight Quantization):

Weight Matrix W (out × in)
    │
    ▼
[1] Block reshape → (out, n_blocks, 128)
    │
    ▼
[2] Per-block L2 normalize → norms saved
    │
    ▼
[3] Walsh-Hadamard rotation: blocks @ H128 × √128
    │  (uniform information distribution)
    │
    ▼
[4] Lloyd-Max 5-bit quantization (32 centroids, N(0,1))
    │  (optimal MSE for Gaussian values)
    │
    ▼
[5] 5-bit pack: 8 codes → 5 bytes
    │
    ▼
polar_state: __packed, __norms, __meta

📖 Citation

@misc{hlwq2026,
  title={HLWQ: Hadamard-Lloyd Weight Quantization for Large Language Models},
  author={Caio Vicentino},
  year={2026},
  url={https://arxiv.org/abs/2603.29078}
}

🔗 Links

Resource	Link
📄 Paper	arXiv:2603.29078
🔧 Code	GitHub
📦 PyPI	`pip install polarquant`
🚀 CT INT4	Qwen3.6-35B-A3B-HLWQ-CT-INT4
🏠 Base model	Qwen/Qwen3.6-35B-A3B

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Paper for caiovicentino1/Qwen3.6-35B-A3B-HLWQ-Q5

PolarQuant: Optimal Gaussian Weight Quantization via Hadamard Rotation for LLM Compression

Paper • 2603.29078 • Published Mar 30