Instructions to use amd/Qwen3.5-397B-A17B-Quark-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/Qwen3.5-397B-A17B-Quark-MXFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="amd/Qwen3.5-397B-A17B-Quark-MXFP4")
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("amd/Qwen3.5-397B-A17B-Quark-MXFP4")
model = AutoModelForMultimodalLM.from_pretrained("amd/Qwen3.5-397B-A17B-Quark-MXFP4", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amd/Qwen3.5-397B-A17B-Quark-MXFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/Qwen3.5-397B-A17B-Quark-MXFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/Qwen3.5-397B-A17B-Quark-MXFP4",
		"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/amd/Qwen3.5-397B-A17B-Quark-MXFP4

SGLang

How to use amd/Qwen3.5-397B-A17B-Quark-MXFP4 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 "amd/Qwen3.5-397B-A17B-Quark-MXFP4" \
    --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": "amd/Qwen3.5-397B-A17B-Quark-MXFP4",
		"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 "amd/Qwen3.5-397B-A17B-Quark-MXFP4" \
        --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": "amd/Qwen3.5-397B-A17B-Quark-MXFP4",
		"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 amd/Qwen3.5-397B-A17B-Quark-MXFP4 with Docker Model Runner:
```
docker model run hf.co/amd/Qwen3.5-397B-A17B-Quark-MXFP4
```

Model Overview

Model Architecture: Qwen3_5MoeForConditionalGeneration
- Input: Text, Image, Video
- Output: Text
Supported Hardware Microarchitecture: AMD MI350/MI355
ROCm: 7.0.0
PyTorch: 2.9.1
Transformers: 5.3.0
Operating System(s): Linux
Inference Engine: SGLang/vLLM
Model Optimizer: AMD-Quark (v0.12)
- Quantized layers: Router Experts and MTP Router Experts
- Weight quantization: OCP MXFP4, Static
- Activation quantization: OCP MXFP4, Dynamic

Model Quantization

The model was quantized from Qwen/Qwen3.5-397B-A17B-FP8 using AMD-Quark. The weights are quantized to MXFP4 and activations are quantized to MXFP4.

Quantization scripts:

import os
from quark.torch import LLMTemplate, ModelQuantizer


# Configuration
ckpt_path = "Qwen/Qwen3.5-397B-A17B-FP8"
output_dir = "amd/Qwen3.5-397B-A17B-Quark-MXFP4"
quant_scheme = "mxfp4"
exclude_layers = ["lm_head", "model.visual.*", "*mlp.gate", "*shared_expert_gate*", "*.linear_attn.*", "*.self_attn.*", "*.shared_expert.*", "mtp.fc"]

# Get quant config from template
template = LLMTemplate.get("qwen3_5_moe")
quant_config = template.get_config(scheme=quant_scheme, exclude_layers=exclude_layers)

# Quantize with File-to-file mode
quantizer = ModelQuantizer(quant_config)
quantizer.direct_quantize_checkpoint(
    pretrained_model_path=ckpt_path,
    save_path=output_dir,
)

For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.

Evaluation

The model was evaluated on the GSM8K benchmark using the SGLang framework.

Accuracy

Benchmark	Qwen/Qwen3.5-397B-A17B-FP8	amd/Qwen3.5-397B-A17B-Quark-MXFP4(this model)	Recovery
gsm8k	97.3	97.3	100.0%

Reproduction

The GSM8K results were obtained with SGLang running inside the Docker image rocm/sgl-dev:v0.5.17-rocm720-mi35x-20260820. For this MTP-quantized model, SGLang must include PR #38870.

Launching server

MODEL="amd/Qwen3.5-397B-A17B-Quark-MXFP4"
AITER_FLYDSL_FORCE=1 HIP_VISIBLE_DEVICES=2,3 \
SGLANG_USE_AITER_UNIFIED_ATTN=1 SGLANG_USE_AITER=1 \
nohup python3 -m sglang.launch_server \
  --model-path "${MODEL}" --tp 2 \
  --attention-backend aiter --trust-remote-code \
  --chunked-prefill-size 32768 \
  --model-loader-extra-config '{"enable_multithread_load": true}' \
  --watchdog-timeout 1200 --mem-fraction-static 0.9 \
  --host 0.0.0.0 --port 8002 --disable-radix-cache \
  --enable-aiter-allreduce-fusion --max-running-requests 128 \
  --page-size 16 \
  --speculative-algorithm NEXTN \
  --speculative-draft-model-path "${MODEL}" \
  --speculative-num-steps 1 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 2 > "tmp_qwen35_mi35x_accuracy_$(date +%Y%m%d_%H%M%S).log" 2>&1 &

Evaluating model in a new terminal

python3 -m sglang.test.run_eval \
  --port 8002 \
  --model amd/Qwen3.5-397B-A17B-Quark-MXFP4 \
  --eval-name gsm8k \
  --num-examples 1319 \
  --num-threads 512 \
  --max-tokens 2048 \
  --chat-template-kwargs '{"enable_thinking": false}' \
  2>&1 | tee /tmp/gsm8k_run_eval.log

License

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Safetensors

Model size

207B params

Tensor type

BF16

F32

Model tree for amd/Qwen3.5-397B-A17B-Quark-MXFP4

Base model

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Qwen/Qwen3.5-397B-A17B-FP8

Quantized

(7)

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