Instructions to use ServiceNow-AI/SuperApriel-15B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ServiceNow-AI/SuperApriel-15B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="ServiceNow-AI/SuperApriel-15B-Instruct", trust_remote_code=True)
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 AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("ServiceNow-AI/SuperApriel-15B-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ServiceNow-AI/SuperApriel-15B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ServiceNow-AI/SuperApriel-15B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ServiceNow-AI/SuperApriel-15B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ServiceNow-AI/SuperApriel-15B-Instruct

SGLang

How to use ServiceNow-AI/SuperApriel-15B-Instruct 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 "ServiceNow-AI/SuperApriel-15B-Instruct" \
    --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": "ServiceNow-AI/SuperApriel-15B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ServiceNow-AI/SuperApriel-15B-Instruct" \
        --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": "ServiceNow-AI/SuperApriel-15B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ServiceNow-AI/SuperApriel-15B-Instruct with Docker Model Runner:
```
docker model run hf.co/ServiceNow-AI/SuperApriel-15B-Instruct
```

ostapeno commited on Apr 23

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@@ -23,7 +23,7 @@ A 15B-parameter **token-mixer supernet** with **8 optimized deployment presets**
 - **Instruction-tuned**: targeted SFT with multiple Pareto-optimal placements
 - **Speculative decoding support**: use all-attention as target with efficient placements as drafts from the same checkpoint
-See the report for detailed benchmarks, quality retention curves, and the full story.
 ## Performance Overview

 - **Instruction-tuned**: targeted SFT with multiple Pareto-optimal placements
 - **Speculative decoding support**: use all-attention as target with efficient placements as drafts from the same checkpoint
+See the [report](https://arxiv.org/abs/2604.19877) for detailed benchmarks, quality retention curves, and the full story.
 ## Performance Overview