Instructions to use apolo13x/Qwen3.5-9B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use apolo13x/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="apolo13x/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("apolo13x/Qwen3.5-9B-NVFP4")
model = AutoModelForImageTextToText.from_pretrained("apolo13x/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 apolo13x/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 "apolo13x/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": "apolo13x/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/apolo13x/Qwen3.5-9B-NVFP4

SGLang

How to use apolo13x/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 "apolo13x/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": "apolo13x/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 "apolo13x/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": "apolo13x/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 apolo13x/Qwen3.5-9B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/apolo13x/Qwen3.5-9B-NVFP4
```

Qwen3.5-9B-NVFP4

This is a quantized version of Qwen/Qwen3.5-9B. This model accepts text and images as inputs and generates text as outputs. The weights and activations were quantized to FP4 using llm-compressor with 512 calibration samples from nvidia/Nemotron-Post-Training-Dataset-v2, reducing the model size from 18.0 GB to 11.5 GB (~1.6x reduction) while maintaining 97.3% average accuracy recovery.

Quantization Details

Scheme: NVFP4
Calibration: 512 samples (256 reasoning-on + 256 reasoning-off) from Nemotron-Post-Training-Dataset-v2
Max sequence length: 4096

Inference

This model is supported in vLLM 0.17.0. To serve the model:

vllm serve Kbenkhaled/Qwen3.5-9B-NVFP4 \
    --reasoning-parser qwen3 \
    --enable-prefix-caching

Evaluation

Evaluated with lm-evaluation-harness, 0-shot, thinking mode ON.

Benchmark	Qwen3.5-9B	Qwen3.5-9B-NVFP4 (this model)	Recovery
GPQA Diamond	78.79%	74.24%	94.2%
IFEval	94.48%	92.69%	98.1%
MMLU-Redux	91.80%	91.39%	99.6%
Average	88.36%	86.11%	97.3%

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Safetensors

Model size

8B params

Tensor type

F32

BF16

F8_E4M3

Model tree for apolo13x/Qwen3.5-9B-NVFP4

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(251)

this model

apolo13x
/

Qwen3.5-9B-NVFP4

Qwen3.5-9B-NVFP4

Quantization Details

Inference

Evaluation

Model tree for apolo13x/Qwen3.5-9B-NVFP4

Dataset used to train apolo13x/Qwen3.5-9B-NVFP4