Instructions to use swiss-ai/Apertus-70B-Instruct-2509 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use swiss-ai/Apertus-70B-Instruct-2509 with Transformers:

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

pipe = pipeline("text-generation", model="swiss-ai/Apertus-70B-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-70B-Instruct-2509")
model = AutoModelForCausalLM.from_pretrained("swiss-ai/Apertus-70B-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-70B-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-70B-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-70B-Instruct-2509",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/swiss-ai/Apertus-70B-Instruct-2509

SGLang

How to use swiss-ai/Apertus-70B-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-70B-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-70B-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-70B-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-70B-Instruct-2509",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use swiss-ai/Apertus-70B-Instruct-2509 with Docker Model Runner:
```
docker model run hf.co/swiss-ai/Apertus-70B-Instruct-2509
```

VLLM Deployment Issues

by ArtusDev - opened Sep 2, 2025

Discussion

ArtusDev

Sep 2, 2025

•

edited Sep 2, 2025

Hi!
It seems like VLLM deployment for Apertus is not working properly. Basic deployment setup (nightly vllm and latest transformers) with and without experimental xielu implementation fails on weights load up:

[1;36m(VllmWorker TP1 pid=546)[0;0m ERROR 09-02 04:29:26 [multiproc_executor.py:574]   File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/apertus.py", line 466, in load_weights
[1;36m(VllmWorker TP1 pid=546)[0;0m ERROR 09-02 04:29:26 [multiproc_executor.py:574]     param = params_dict[name]
[1;36m(VllmWorker TP1 pid=546)[0;0m ERROR 09-02 04:29:26 [multiproc_executor.py:574]             ~~~~~~~~~~~^^^^^^
[1;36m(VllmWorker TP1 pid=546)[0;0m ERROR 09-02 04:29:26 [multiproc_executor.py:574] KeyError: 'layers.0.mlp.act_fn.beta'

Is this a known issue or there are specific requirements for VLLM deployment set up right now? I couldn't find any direct deployment guidelines regarding Apertus.

descil

Sep 2, 2025

•

edited Sep 2, 2025

I also have issues with vLLM deployment via GpuStack.

Failed to initialize vllm candidates selector: The checkpoint you are trying to load has model type apertus but Transformers does not recognize this architecture. This could be because of an issue with the checkpoint, or because your version of Transformers is out of date. You can update Transformers with the command pip install --upgrade transformers. If this does not work, and the checkpoint is very new, then there may not be a release version that supports this model yet. In this case, you can get the most up-to-date code by installing Transformers from source with the command pip install git+https://github.com/huggingface/transformers.git.

pip show transformers
Version: 4.55.3

Edit: Only tried 8B-Instruct.