Instructions to use qmxme/Qwen3.6-27B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qmxme/Qwen3.6-27B-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="qmxme/Qwen3.6-27B-NVFP4")
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("qmxme/Qwen3.6-27B-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("qmxme/Qwen3.6-27B-NVFP4")
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 qmxme/Qwen3.6-27B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qmxme/Qwen3.6-27B-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qmxme/Qwen3.6-27B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qmxme/Qwen3.6-27B-NVFP4

SGLang

How to use qmxme/Qwen3.6-27B-NVFP4 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 "qmxme/Qwen3.6-27B-NVFP4" \
    --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": "qmxme/Qwen3.6-27B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "qmxme/Qwen3.6-27B-NVFP4" \
        --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": "qmxme/Qwen3.6-27B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use qmxme/Qwen3.6-27B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/qmxme/Qwen3.6-27B-NVFP4
```

Qwen3.6-27B NVFP4

This is an AutoRound NVFP4 quantization of Qwen/Qwen3.6-27B.

The checkpoint uses the compressed-tensors nvfp4-pack-quantized format produced through AutoRound's llm_compressor export path. It is intended for runtimes that support this format, such as recent vLLM builds with NVFP4/compressed-tensors support on compatible NVIDIA hardware.

Quantization details

Base model: Qwen/Qwen3.6-27B
Base snapshot: 6a9e13bd6fc8f0983b9b99948120bc37f49c13e9
Quantizer: AutoRound 0.12.3
Format: compressed-tensors nvfp4-pack-quantized
Weight format: 4-bit float, group size 16, FP8 scales

Quantized weights:

language MLP projections: gate_proj, up_proj, down_proj

Kept in bf16:

token embeddings
lm_head
visual tower
MTP tensors
linear_attn.*
self_attn.*

Size

Original indexed tensor size: about 51.75 GiB
Quantized indexed tensor size: about 28.05 GiB
Repository folder size: about 30 GiB
Indexed tensor size reduction: about 45.8%

Notes

This model has not been benchmarked here. Please run your own validation for the runtime, context length, and workload you plan to use.

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Safetensors

Model size

10B params

Tensor type

BF16

F32

F8_E4M3

Model tree for qmxme/Qwen3.6-27B-NVFP4

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Quantized

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