Instructions to use swiss-ai/Apertus-8B-Instruct-2509 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use swiss-ai/Apertus-8B-Instruct-2509 with Transformers:

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

pipe = pipeline("text-generation", model="swiss-ai/Apertus-8B-Instruct-2509")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("swiss-ai/Apertus-8B-Instruct-2509")
model = AutoModelForCausalLM.from_pretrained("swiss-ai/Apertus-8B-Instruct-2509")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use swiss-ai/Apertus-8B-Instruct-2509 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "swiss-ai/Apertus-8B-Instruct-2509"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "swiss-ai/Apertus-8B-Instruct-2509",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/swiss-ai/Apertus-8B-Instruct-2509

SGLang

How to use swiss-ai/Apertus-8B-Instruct-2509 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 "swiss-ai/Apertus-8B-Instruct-2509" \
    --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": "swiss-ai/Apertus-8B-Instruct-2509",
		"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 "swiss-ai/Apertus-8B-Instruct-2509" \
        --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": "swiss-ai/Apertus-8B-Instruct-2509",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use swiss-ai/Apertus-8B-Instruct-2509 with Docker Model Runner:
```
docker model run hf.co/swiss-ai/Apertus-8B-Instruct-2509
```

Apertus tool parser

#18

by frsodano - opened Sep 19, 2025

Discussion

frsodano

Sep 19, 2025

In the description you said Apertus supports tools and that supports vLLM.

If you would like to enable tools in vLLM you need to provide the following 3 parameters:

--enable-auto-tool-choice
--tool-call-parser
--chat-template

Here it is the example for Llama from vLLM

https://docs.vllm.ai/en/latest/features/tool_calling.html

chat-template seems to be here: https://github.com/swiss-ai/apertus-format/blob/main/src/templates/chat_template.jinja (please correct me if wrong)

But what about the tool-call-parser?

Any help here?

Thanks!

EDurech

Swiss AI Initiative org Sep 22, 2025

@frsodano hey, tool use isn't fully supported, the model has been trained on initial data so it has capabilities but we haven't integrated into inference engines yet as we're still training for e.g. tooling

frsodano

Sep 22, 2025

Thanks for the info! we were able to modify the chat_template and created our own Apertus tool parser. it's working good.. We are able to use it with Semantic Kernel (Python). Apertus is calling the tools correctly! great job! :)

son0203

Sep 22, 2025

Hi all!- we d also be highly interested to get tools working- @frsodano any chance to get details about your solution? 😇 Br tom

EDurech

Swiss AI Initiative org Sep 22, 2025

@frsodano go ahead and share yours if you'd like, we will release and integrate into the inference engines when it's fully supported, right now the model rather responds to the format in the prompt than the format it was trained on (as we still need to do more training for tooling) ;)

frsodano

Sep 23, 2025

We modified the chat_template and the llama3_json parser from vLLM to make it works with Apertus. We are solving a bug we found for Tools not having parameters, but for the rest is working fine. We are now testing it properly and then we will share it. Sharing is Caring. :)