Instructions to use ServiceNow-AI/Apriel-1.5-15b-Thinker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ServiceNow-AI/Apriel-1.5-15b-Thinker with Transformers:

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

pipe = pipeline("image-text-to-text", model="ServiceNow-AI/Apriel-1.5-15b-Thinker")
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("ServiceNow-AI/Apriel-1.5-15b-Thinker")
model = AutoModelForMultimodalLM.from_pretrained("ServiceNow-AI/Apriel-1.5-15b-Thinker", 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 ServiceNow-AI/Apriel-1.5-15b-Thinker with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ServiceNow-AI/Apriel-1.5-15b-Thinker"
# 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/Apriel-1.5-15b-Thinker",
		"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/ServiceNow-AI/Apriel-1.5-15b-Thinker

SGLang

How to use ServiceNow-AI/Apriel-1.5-15b-Thinker 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/Apriel-1.5-15b-Thinker" \
    --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/Apriel-1.5-15b-Thinker",
		"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 "ServiceNow-AI/Apriel-1.5-15b-Thinker" \
        --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/Apriel-1.5-15b-Thinker",
		"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 ServiceNow-AI/Apriel-1.5-15b-Thinker with Docker Model Runner:
```
docker model run hf.co/ServiceNow-AI/Apriel-1.5-15b-Thinker
```

stm4 commited on Sep 30, 2025

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@@ -27,6 +27,8 @@ library_name: transformers
 ---
 # Summary
 **Apriel-1.5-15b-Thinker** is a multimodal reasoning model in ServiceNow’s Apriel SLM series which achieves competitive performance against models 10 times it's size. Apriel-1.5 is the second model in the reasoning series. It introduces enhanced textual reasoning capabilities and adds image reasoning support to the previous text model.  It has undergone extensive continual pretraining across both text and image domains. In terms of post-training this model has **undergone text-SFT only**. Our research demonstrates that with a strong mid-training regimen, we are able to achive SOTA performance on text and image reasoning tasks without having any image SFT training or RL.

 ---
+**Click here to skip to the technical report** -> https://huggingface.co/ServiceNow-AI/Apriel-1.5-15b-Thinker/blob/main/Apriel-1.5-Thinker.pdf
 # Summary
 **Apriel-1.5-15b-Thinker** is a multimodal reasoning model in ServiceNow’s Apriel SLM series which achieves competitive performance against models 10 times it's size. Apriel-1.5 is the second model in the reasoning series. It introduces enhanced textual reasoning capabilities and adds image reasoning support to the previous text model.  It has undergone extensive continual pretraining across both text and image domains. In terms of post-training this model has **undergone text-SFT only**. Our research demonstrates that with a strong mid-training regimen, we are able to achive SOTA performance on text and image reasoning tasks without having any image SFT training or RL.