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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("SuperQAI2050/Q_Research-Qwenvidia")
model = AutoModelForImageTextToText.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

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 / explainability.md

SuperQAI2050

Duplicate from nvidia/Ising-Calibration-1-35B-A3B

e3725ea about 1 month ago

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Field	Response
Intended Task/Domain:	Scientific research and quantum computing calibration experiment analysis
Model Type:	Mixture-of-Experts Vision-Language Model (MoE VLM) based on `Qwen3.5-35B-A3B`
Intended Users:	Quantum computing researchers, calibration engineers, and developers analyzing experiment results in automated or assisted calibration workflows.
Output:	Text responses covering technical descriptions, experimental conclusions, significance assessments, fit quality evaluations, parameter extraction, and experiment success classifications.
Describe how the model works:	Experiment plot images are encoded into visual tokens and combined with prompt text tokens before being processed by the `Qwen3.5-35B-A3B` MoE language model. The model activates 8 of 256 experts per token, corresponding to about 3B active parameters out of roughly 35B total parameters, and generates analytical text autoregressively through a vLLM-served OpenAI-compatible API.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:	Not Applicable
Technical Limitations & Mitigation:	The model is domain-specific to quantum calibration experiments and may not generalize to broader VLM tasks. Performance varies by question type, with weaker results on experimental significance and parameter extraction than on fit quality assessment. Outputs should be validated by domain experts before being used in experimental workflows.
Performance Metrics:	QCalEval zero-shot scores (averaged): Q1 Technical Description `87.8`, Q2 Experimental Conclusion `67.1`, Q3 Experimental Significance `64.7`, Q4 Fit Quality Assessment `90.5`, Q5 Parameter Extraction `62.5`, Q6 Experiment Success `75.3`, Overall `74.7`.
Verified to have met prescribed NVIDIA quality standards:	Yes
Potential Known Risks:	The model may misclassify rare or ambiguous experiment outcomes, may hallucinate details outside the quantum calibration domain, and does not have access to raw numerical traces or experiment metadata beyond what is visible in the input plots.
Governing Terms:	The Ising-Calibration-1-35B-A3B is governed by the NVIDIA Open Model License Agreement.
Additional Information:	For Qwen3.5-35B-A3B Apache License, Version 2.0.