Instructions to use AxionML/Qwen3.5-397B-A17B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AxionML/Qwen3.5-397B-A17B-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="AxionML/Qwen3.5-397B-A17B-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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("AxionML/Qwen3.5-397B-A17B-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("AxionML/Qwen3.5-397B-A17B-NVFP4", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AxionML/Qwen3.5-397B-A17B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AxionML/Qwen3.5-397B-A17B-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": "AxionML/Qwen3.5-397B-A17B-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/AxionML/Qwen3.5-397B-A17B-NVFP4

SGLang

How to use AxionML/Qwen3.5-397B-A17B-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 "AxionML/Qwen3.5-397B-A17B-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": "AxionML/Qwen3.5-397B-A17B-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 "AxionML/Qwen3.5-397B-A17B-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": "AxionML/Qwen3.5-397B-A17B-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 AxionML/Qwen3.5-397B-A17B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/AxionML/Qwen3.5-397B-A17B-NVFP4
```

AxionML Qwen3.5-397B-A17B-NVFP4

Mirrored by AxionML for open-source serving and deployment use cases. Part of AxionML's effort to provide ready-to-serve quantized models for the community.

Quantized by NVIDIA. The weights in this repository are an unmodified copy of nvidia/Qwen3.5-397B-A17B-NVFP4-V2 (revision 8f590eae8f10bf55d9a46f79ea0280bde435c9f8). All credit for the quantization belongs to NVIDIA.

This is an NVFP4-quantized version of Qwen/Qwen3.5-397B-A17B (397B total parameters, 17B activated), quantized with NVIDIA Model Optimizer.

About NVFP4 quantization: NVFP4 on Blackwell couples a compact E2M1 FP4 codebook with blockwise FP8 (E4M3) scaling over 16-element micro-blocks, so that 4-bit stored values remain numerically useful for neural-network computation. The E2M1 codebook provides a small, nonuniform set of representable magnitudes up to ±6 and relies on saturating behavior rather than IEEE NaN/Inf encodings to maximize usable range per bit. Using an FP8 block scale (rather than power-of-two-only E8M0) enables fractional scales and error-minimizing scale selection. On Blackwell Tensor Cores, native FP4 multipliers exploit E2M1 simplicity while higher-precision FP32 accumulation protects dot-product accuracy.

Ready for commercial and non-commercial use under Apache 2.0.

Model Summary


Architecture	Hybrid-attention MoE (Gated DeltaNet + Gated Attention) with vision encoder
Total Parameters	397B
Activated Parameters	17B
Context Length	262K tokens
Checkpoint Size	~244 GB

Evaluation Results

Benchmark	FP8 (baseline)	NVFP4 (V2)
MMMU Pro	78.7	78.4
GPQA Diamond	87.2	87.7
SciCode	46.7	48.1
AA-LCR	68.8	67.8
IFBench	76.1	76.5
τ²-Bench Telecom	95.4	95.2

Scores reported by NVIDIA for this checkpoint. Baseline: Qwen/Qwen3.5-397B-A17B-FP8. temperature=0.6, top_p=0.95, max 64,000 tokens (128,000 for τ²-Bench Telecom).

Quantization Details

Quantization format: mixed precision (NVIDIA's V2 recipe) — routed experts in NVFP4 with MSE-based scale search; attention and shared experts in per-tensor FP8
KV cache: FP8
Calibration dataset: cnn_dailymail, Nemotron-Post-Training-Dataset-v2
Tool: NVIDIA Model Optimizer v0.45.0.dev173
Why V2: NVIDIA's earlier V1 (nvidia/Qwen3.5-397B-A17B-NVFP4, Feb 2026) quantized only the routed experts with max calibration and left attention and shared experts in BF16 (~251 GB). V2 adds MSE scale search and FP8 attention/shared experts — smaller and faster, and within noise of Qwen's FP8 checkpoint.

Usage

Deploy with SGLang

python3 -m sglang.launch_server \
    --model-path AxionML/Qwen3.5-397B-A17B-NVFP4 \
    --tensor-parallel-size 4 \
    --quantization modelopt_mixed \
    --disable-radix-cache \
    --trust-remote-code

Validated upstream with lmsysorg/sglang:v0.5.12.post1-cu130 on B300. Tip: add --mamba-ssm-dtype bfloat16 to keep the Mamba SSM state in BF16 for faster decode on Blackwell.

Limitations

The base model was trained on data that may contain toxic language and societal biases. The quantized model inherits these limitations. It may generate inaccurate, biased, or offensive content. Please refer to the original model card and the upstream quantized model card for full details.

Credits

Base model: Qwen/Qwen3.5-397B-A17B
Quantization: nvidia/Qwen3.5-397B-A17B-NVFP4-V2 by NVIDIA
Mirror: AxionML

Downloads last month: 682

Safetensors

Model size

210B params

Tensor type

BF16

F8_E4M3

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