Instructions to use nvidia/Ising-Calibration-1.5-31B-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Ising-Calibration-1.5-31B-BF16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="nvidia/Ising-Calibration-1.5-31B-BF16")
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("nvidia/Ising-Calibration-1.5-31B-BF16")
model = AutoModelForMultimodalLM.from_pretrained("nvidia/Ising-Calibration-1.5-31B-BF16", 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 nvidia/Ising-Calibration-1.5-31B-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Ising-Calibration-1.5-31B-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Ising-Calibration-1.5-31B-BF16",
		"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/nvidia/Ising-Calibration-1.5-31B-BF16

SGLang

How to use nvidia/Ising-Calibration-1.5-31B-BF16 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 "nvidia/Ising-Calibration-1.5-31B-BF16" \
    --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": "nvidia/Ising-Calibration-1.5-31B-BF16",
		"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 "nvidia/Ising-Calibration-1.5-31B-BF16" \
        --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": "nvidia/Ising-Calibration-1.5-31B-BF16",
		"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 nvidia/Ising-Calibration-1.5-31B-BF16 with Docker Model Runner:
```
docker model run hf.co/nvidia/Ising-Calibration-1.5-31B-BF16
```

Ising-Calibration-1.5-31B-BF16 / explainability.md

ShuxiangCaoNV

Publish NVIDIA Ising Calibration 1.5 BF16 model

a9909df 9 days ago

preview code

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Explainability Subcard

Intended Task/Domain:

Scientific research and quantum computing calibration experiment analysis

Model Type:

Dense multimodal vision-language model based on Gemma 4 31B

Intended Users:

Quantum computing researchers, calibration engineers, and developers analyzing experiment results in automated or assisted calibration workflows.

Output:

Types: Text. Formats: String

Describe how the model works:

Experiment plot images are encoded into visual tokens and combined with prompt text tokens before being processed by the Gemma 4 31B dense language model. The model generates analytical text autoregressively through a NVIDIA NIM vLLM-served OpenAI-compatible API.

Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:

Not Applicable

Technical Limitations and 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.

Verified to have met prescribed NVIDIA quality standards:

Yes

Performance Metrics:

QCalEval zero-shot overall 74.5; QCalEval MM-ICL overall 81.2; throughput and latency were measured for the BF16 NIM release candidate on NVIDIA GPU-accelerated systems.

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.

License/Terms of Use

GOVERNING TERMS: Use of this model is governed by the OpenMDW License Agreement, version 1.1. ADDITIONAL INFORMATION: Apache License, Version 2.0.