Instructions to use amd/Qwen3.5-35B-A3B-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/Qwen3.5-35B-A3B-MXFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="amd/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-MXFP4")
model = AutoModelForMultimodalLM.from_pretrained("amd/Qwen3.5-35B-A3B-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?"}
        ]
    },
]
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/Qwen3.5-35B-A3B-MXFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-MXFP4

SGLang

How to use amd/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-MXFP4 with Docker Model Runner:
```
docker model run hf.co/amd/Qwen3.5-35B-A3B-MXFP4
```

Model Overview

Model Architecture: Qwen3_5MoeForConditionalGeneration
- Input: Text
- Output: Text
Supported Hardware Microarchitecture: AMD MI300 MI350/MI355
ROCm: 7.0.0
PyTorch: 2.9.1
Transformers: 5.3.0
vLLM: 0.16.0rc2
lm-evaluation-harness: 0.4.11
Operating System(s): Linux
Inference Engine: SGLang/vLLM
Model Optimizer: AMD-Quark (v0.12)
- Weight quantization: OCP MXFP4, Static
- Activation quantization: OCP MXFP4, Dynamic

Model Quantization

The model was quantized from Qwen/Qwen3.5-35B-A3B-FP8 using AMD-Quark. The weights are quantized to MXFP4 and activations are quantized to MXFP4.

Quantization scripts:

cd Quark/examples/torch/language_modeling/llm_ptq/
export exclude_layers="lm_head model.visual.* mtp.* *mlp.gate *shared_expert_gate* *.linear_attn.* *.self_attn.* *.shared_expert.*"
python3 quantize_quark.py --model_dir Qwen/Qwen3.5-35B-A3B-FP8 \
                          --quant_scheme mxfp4 \
                          --file2file_quantization \
                          --exclude_layers $exclude_layers \
                          --output_dir amd/Qwen3.5-35B-A3B-MXFP4

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

Evaluation

The model was evaluated on gsm8k benchmarks using the vllm framework.

Accuracy

Benchmark	Qwen/Qwen3.5-35B-A3B-FP8	amd/Qwen3.5-35B-A3B-MXFP4(this model)	Recovery
gsm8k (flexible-extract)	89.39	93.25	104.32%

Reproduction

The GSM8K results were obtained using the vLLM framework, based on the Docker image [rocm/vllm-dev:nightly_main_20260211], and vLLM is installed inside the container.

docker pull rocm/vllm-dev:nightly_main_20260211

Evaluating model in a new terminal

lm_eval \
  --model vllm \
  --model_args pretrained=amd/Qwen3.5-35B-A3B-MXFP4,tensor_parallel_size=4,max_model_len=262144,gpu_memory_utilization=0.90,max_gen_toks=2048,trust_remote_code=True,reasoning_parser=qwen3 \
  --tasks gsm8k  --num_fewshot 5 \
  --batch_size auto

License

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Safetensors

Model size

21B params

Tensor type

BF16

F32

Model tree for amd/Qwen3.5-35B-A3B-MXFP4

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Quantized

Qwen/Qwen3.5-35B-A3B-FP8

Quantized

(2)

this model