Instructions to use Sehyo/Qwen3.5-122B-A10B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sehyo/Qwen3.5-122B-A10B-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Sehyo/Qwen3.5-122B-A10B-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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Sehyo/Qwen3.5-122B-A10B-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("Sehyo/Qwen3.5-122B-A10B-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 Settings

vLLM

How to use Sehyo/Qwen3.5-122B-A10B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sehyo/Qwen3.5-122B-A10B-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": "Sehyo/Qwen3.5-122B-A10B-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/Sehyo/Qwen3.5-122B-A10B-NVFP4

SGLang

How to use Sehyo/Qwen3.5-122B-A10B-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 "Sehyo/Qwen3.5-122B-A10B-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": "Sehyo/Qwen3.5-122B-A10B-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 "Sehyo/Qwen3.5-122B-A10B-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": "Sehyo/Qwen3.5-122B-A10B-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 Sehyo/Qwen3.5-122B-A10B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/Sehyo/Qwen3.5-122B-A10B-NVFP4
```

Qwen3.5-122B-A10B-NVFP4

This is a quantized version of Qwen/Qwen3.5-122B-A10B using the NVFP4 quantization scheme.

Please use nightly vLLM for support.

Changelog

02/03/2026: Added MTP (multi-token prediction) weights from source checkpoint, enabling speculative decoding with vLLM.
25/02/2026: Initial upload.

Calibration

Samples: 512 (256 from each dataset)
Datasets:
- HuggingFaceH4/ultrachat_200k (train_sft split)
- nvidia/Nemotron-Post-Training-Dataset-v2 (chat split)
Max sequence length: 4096
All experts calibrated: moe_calibrate_all_experts=True

Creation

This model was created using VLLM's LLM Compressor with Qwen3.5 MoE support added via PR #2383. The PR adds a custom CalibrationQwen3MoeSparseMoeBlock that routes calibration data to all experts during quantization, ensuring every expert receives proper calibration for accurate NVFP4 quantization.

Downloads last month: 37,850

Safetensors

Model size

71B params

Tensor type

F32

BF16

F8_E4M3

Model tree for Sehyo/Qwen3.5-122B-A10B-NVFP4

Base model

Qwen/Qwen3.5-122B-A10B

Quantized

(120)

this model