Instructions to use ykarout/Qwen3.5-9B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ykarout/Qwen3.5-9B-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="ykarout/Qwen3.5-9B-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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("ykarout/Qwen3.5-9B-NVFP4")
model = AutoModelForImageTextToText.from_pretrained("ykarout/Qwen3.5-9B-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

vLLM

How to use ykarout/Qwen3.5-9B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ykarout/Qwen3.5-9B-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": "ykarout/Qwen3.5-9B-NVFP4",
		"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/ykarout/Qwen3.5-9B-NVFP4

SGLang

How to use ykarout/Qwen3.5-9B-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 "ykarout/Qwen3.5-9B-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": "ykarout/Qwen3.5-9B-NVFP4",
		"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 "ykarout/Qwen3.5-9B-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": "ykarout/Qwen3.5-9B-NVFP4",
		"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 ykarout/Qwen3.5-9B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/ykarout/Qwen3.5-9B-NVFP4
```

Qwen3.5-9B-NVFP4

Quantized variant of Qwen/Qwen3.5-9B exported in unified Hugging Face checkpoint format.

Quantization Details

This checkpoint corresponds to an NVFP4 MLP-only export profile:

MLP layers: NVFP4
Non-MLP layers: kept in higher precision (e.g. BF16)
KV cache: left unquantized in export config (kvnone profile)
Vision modules: kept in higher precision to preserve multimodal quality

Recommended Runtime (vLLM Nightly)

Use the latest nightly vLLM build:

pip install -U --pre vllm --extra-index-url https://wheels.vllm.ai/nightly

Serve directly from this Hub repo:

vllm serve "ykarout/Qwen3.5-9b-nvfp4" \
  --port 8000 \
  --tensor-parallel-size 1 \
  --max-model-len 65536 \
  --gpu-memory-utilization 0.85 \ #adjust based on VRAM 
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --chat-template "chat_template.jinja" \ #chat_template.ninja file in the repo root
  --enable-prefix-caching \
  --served-model-name qwen3.5-9b-nvfp4

Quick Test

curl -s http://127.0.0.1:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model":"qwen3.5-9b-nvfp4",
    "messages":[{"role":"user","content":"Explain KV cache in 3 bullet points."}],
    "max_tokens":220,
    "temperature":0.7,
    "top_p":0.8,
    "top_k":20,
    "min_p":0.0,
    "presence_penalty":1.5,
    "repetition_penalty":1.0
  }'

Notes

If VRAM is tight, reduce --max-model-len and/or --gpu-memory-utilization.
This is a quantized checkpoint; output quality and speed depend on backend/kernel versions.

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Safetensors

Model size

8B params

Tensor type

BF16

F8_E4M3

Model tree for ykarout/Qwen3.5-9B-NVFP4

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Quantized

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