Instructions to use swiss-ai/Apertus-8B-2509 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swiss-ai/Apertus-8B-2509 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="swiss-ai/Apertus-8B-2509")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("swiss-ai/Apertus-8B-2509") model = AutoModelForCausalLM.from_pretrained("swiss-ai/Apertus-8B-2509") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use swiss-ai/Apertus-8B-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-2509" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "swiss-ai/Apertus-8B-2509", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/swiss-ai/Apertus-8B-2509
- SGLang
How to use swiss-ai/Apertus-8B-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-2509" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "swiss-ai/Apertus-8B-2509", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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-2509" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "swiss-ai/Apertus-8B-2509", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use swiss-ai/Apertus-8B-2509 with Docker Model Runner:
docker model run hf.co/swiss-ai/Apertus-8B-2509
Correcting the link for the report. The previous one is obsolete. (#6)
Browse files- Correcting the link for the report. The previous one is obsolete. (a07742a9e2900b2a6ea76acd7618b645cb67fa67)
Co-authored-by: Arthur Wuhrmann <AWuhrmann@users.noreply.huggingface.co>
README.md
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| Llama4-Scout-16x17B | 67.9 | 74.7 | 66.8 | 73.2 | 43.5 | 67.7 | 81.2 |
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| GPT-OSS-20B | 58.1 | 67.0 | 41.5 | 66.5 | 37.4 | 60.4 | 75.6 |
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Many additional benchmark evaluations, for pretraining and posttraining phases, multilingual evaluations in around hundred languages, and long context evaluations are provided in Section 5 of the [
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## Training
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| Llama4-Scout-16x17B | 67.9 | 74.7 | 66.8 | 73.2 | 43.5 | 67.7 | 81.2 |
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| GPT-OSS-20B | 58.1 | 67.0 | 41.5 | 66.5 | 37.4 | 60.4 | 75.6 |
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Many additional benchmark evaluations, for pretraining and posttraining phases, multilingual evaluations in around hundred languages, and long context evaluations are provided in Section 5 of the [Apertus Tech Report](https://arxiv.org/abs/2509.14233)
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## Training
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