Instructions to use SuperQAI2050/Q_Research-Qwenvidia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SuperQAI2050/Q_Research-Qwenvidia with Transformers:

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

pipe = pipeline("image-text-to-text", model="SuperQAI2050/Q_Research-Qwenvidia")
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("SuperQAI2050/Q_Research-Qwenvidia")
model = AutoModelForMultimodalLM.from_pretrained("SuperQAI2050/Q_Research-Qwenvidia")
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 SuperQAI2050/Q_Research-Qwenvidia with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SuperQAI2050/Q_Research-Qwenvidia"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SuperQAI2050/Q_Research-Qwenvidia",
		"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/SuperQAI2050/Q_Research-Qwenvidia

SGLang

How to use SuperQAI2050/Q_Research-Qwenvidia 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 "SuperQAI2050/Q_Research-Qwenvidia" \
    --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": "SuperQAI2050/Q_Research-Qwenvidia",
		"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 "SuperQAI2050/Q_Research-Qwenvidia" \
        --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": "SuperQAI2050/Q_Research-Qwenvidia",
		"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 SuperQAI2050/Q_Research-Qwenvidia with Docker Model Runner:
```
docker model run hf.co/SuperQAI2050/Q_Research-Qwenvidia
```

Q_Research-Qwenvidia

File size: 1,173 Bytes

e3725ea

NVIDIA Ising Calibration 1 NIM
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.

This NIM container is governed by the NVIDIA Software License Agreement
and the Product-Specific Terms for NVIDIA AI Products. Use of the model
is governed by the NVIDIA Open Model License.

================================================================================
Third-Party Software Attribution
================================================================================

This product includes a model derived from the following third-party work:

Qwen3.5-35B-A3B
Copyright 2026 Alibaba Cloud
Licensed under the Apache License, Version 2.0
https://huggingface.co/Qwen/Qwen3.5-35B-A3B

A copy of the Apache License, Version 2.0 is included in the file
APACHE-2.0.txt and is also available at:
http://www.apache.org/licenses/LICENSE-2.0

The NVIDIA-Ising-Calibration-1 model is a derivative work of Qwen3.5-35B-A3B,
created through supervised fine-tuning on quantum calibration experiment data.
The model weights and configuration have been modified from the original.

The original model card is available at:
https://huggingface.co/Qwen/Qwen3.5-35B-A3B