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
```

denisko Claude Opus 4.6 commited on Apr 15

Commit

da9a2d8

1 Parent(s): 9c39f06

Add note about vLLM pure-recurrent placement limitation

Browse files

Custom placements using only GDN/KDA mixers (no FA or SWA) are not
supported due to a vLLM KV cache coordinator constraint. All shipped
presets include attention-type layers and are unaffected.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Files changed (1) hide show

README.md +3 -0

README.md CHANGED Viewed

@@ -166,6 +166,8 @@ print(output[0].outputs[0].text)
 Available presets: `all-attention`, `Reg_Lklhd-26`, `Reg_Lklhd-18`, `Reg_Lklhd-13`, `Reg_Lklhd-10`, `Idealized_All-18`, `Idealized_All-6`, `Idealized_Lklhd-6`, `extra_bayesian-mix-7`.
 ### Supernet Mode (Runtime Switching)
 Load the full supernet and switch presets at runtime without reloading the engine. Use `enforce_eager=True` on a single GPU (saves memory by skipping CUDA graph capture):
@@ -376,6 +378,7 @@ It is **not intended** for use in safety-critical applications without human ove
 - **Language:** Strongest performance is in English. Output quality may degrade in underrepresented languages.
 - **Critical use:** Not suitable for medical, legal, financial, or other high-risk applications without safeguards.
 - **Long-context degradation:** Aggressive placements (fewer FA layers) may show reduced performance on long-range tasks like RULER/NIAH.
 ## Security and Responsible Use

 Available presets: `all-attention`, `Reg_Lklhd-26`, `Reg_Lklhd-18`, `Reg_Lklhd-13`, `Reg_Lklhd-10`, `Idealized_All-18`, `Idealized_All-6`, `Idealized_Lklhd-6`, `extra_bayesian-mix-7`.
+> **Note:** Custom placements must include at least one attention-type layer (FA or SWA). Configurations using only recurrent mixers (GDN/KDA) are not currently supported due to a vLLM KV cache coordinator limitation. All shipped presets satisfy this requirement.
 ### Supernet Mode (Runtime Switching)
 Load the full supernet and switch presets at runtime without reloading the engine. Use `enforce_eager=True` on a single GPU (saves memory by skipping CUDA graph capture):
 - **Language:** Strongest performance is in English. Output quality may degrade in underrepresented languages.
 - **Critical use:** Not suitable for medical, legal, financial, or other high-risk applications without safeguards.
 - **Long-context degradation:** Aggressive placements (fewer FA layers) may show reduced performance on long-range tasks like RULER/NIAH.
+- **vLLM pure-recurrent limitation:** Custom placements using only GDN/KDA mixers (no FA or SWA layers) are not supported with vLLM due to a KV cache coordinator constraint. All shipped presets include attention-type layers.
 ## Security and Responsible Use