Instructions to use cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4")
model = AutoModelForMultimodalLM.from_pretrained("cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4")
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 Settings

vLLM

How to use cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4",
		"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/cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4

SGLang

How to use cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4 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 "cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4" \
    --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": "cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4",
		"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 "cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4" \
        --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": "cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4",
		"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 cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4 with Docker Model Runner:
```
docker model run hf.co/cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4
```

Anyone able to fit this in 32gb vram using 2 cards?

by tasticleeze - opened May 4

Discussion

tasticleeze

May 4

•

edited May 4

Thank you for the quant! Anyone able to fit this in 32gb vram using 2 cards?

kezzyhu

May 6

•

edited May 6

Rtx5060ti 16G*2 ubuntu验证通过:
export NCCL_P2P_DISABLE=1
export NCCL_CUMEM_HOST_ENABLE=0
export MAX_JOBS=1
export NCCL_IB_DISABLE=1
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
export VLLM_SLEEP_WHEN_IDLE=1

vllm serve /h/models/cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4
--host ::
--port 1234
--pipeline-parallel-size 1
--tensor-parallel-size 2
--served-model-name Qwen3.6
--quantization compressed-tensors
--gpu-memory-utilization=0.9
--max-model-len=60000
--max-num-seqs=2
--block-size 32
--enable-chunked-prefill
--max-num-batched-tokens=2048
--enable-prefix-caching
--enable-auto-tool-choice
--tool-call-parser qwen3_coder
--reasoning-parser qwen3
--default-chat-template-kwargs '{"preserve_thinking":true}'
--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'
--language-model-only \
--skip-mm-profiling
--compilation-config '{"cudagraph_mode": "NONE"}'
--kv-cache-dtype fp8_e4m3
--cpu-offload-gb 15
--offload-backend prefetch
--offload-group-size 16
--offload-num-in-group 3

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