Instructions to use swiss-ai/Apertus-v1.5-70B-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use swiss-ai/Apertus-v1.5-70B-base with Transformers:

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

pipe = pipeline("image-text-to-text", model="swiss-ai/Apertus-v1.5-70B-base")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("swiss-ai/Apertus-v1.5-70B-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use swiss-ai/Apertus-v1.5-70B-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "swiss-ai/Apertus-v1.5-70B-base"
# 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-v1.5-70B-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/swiss-ai/Apertus-v1.5-70B-base

SGLang

How to use swiss-ai/Apertus-v1.5-70B-base 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-v1.5-70B-base" \
    --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-v1.5-70B-base",
		"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-v1.5-70B-base" \
        --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-v1.5-70B-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use swiss-ai/Apertus-v1.5-70B-base with Docker Model Runner:
```
docker model run hf.co/swiss-ai/Apertus-v1.5-70B-base
```

You need to agree to share your Hugging Face information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

By clicking "Agree" I confirm I have read and agree to the Apache 2․0 license and the Acceptable Use Policy (AUP), available at: https://github.com/swiss-ai/apertus-legal/blob/main/apertus_1.5/USAGE_POLICY.pdf.

By clicking "Agree" you accept to share your contact information (email and username) with the repository authors. The Apertus v1․5 privacy policy relating to the use of your contact information is available at https://github.com/swiss-ai/apertus-legal/blob/main/apertus_1.5/PRIVACY_POLICY.pdf.

Apertus-v1.5-70B-base

Apertus 1.5 is a family of 8B and 70B fully open, multilingual, and multimodal models from the Swiss AI Initiative, trained only on openly available data and released with open weights, open data, and open training documentation. The models cover a large variety of languages, handle contexts up to 262,144 tokens, accept image and audio input, and support a thinking mode for reasoning tasks.

This checkpoint is the long context extended model of the 70B pre-training pipeline.

Stage	8B	70B
Pre-Training	Apertus-v1.5-8B-base	Apertus-v1.5-70B-base (this model)
Supervised finetuning	Apertus-v1.5-8B-SFT	Apertus-v1.5-70B-SFT
Reinforcement learning	Apertus-v1.5-8B-RLVR	Apertus-v1.5-70B-RLVR
Preference alignment (DPO)	Apertus-v1.5-8B-DPO	Apertus-v1.5-70B
Charter alignment (SDPO)	Apertus-v1.5-8B	--

How to Use

This is a base model without a chat template: use plain-text completion. For the instruction-tuned model, see swiss-ai/Apertus-v1.5-70B.

Installation

VLLM_USE_PRECOMPILED=1 \
VLLM_PRECOMPILED_WHEEL_COMMIT=8700f86a7012b2354c3f2834c1a0d2b6023b0bcd \
pip install "vllm @ git+https://github.com/swiss-ai/vllm.git@b84ba28e5ed19c08e09a9f2561451001b65aaa0e"

pip install "transformers[torch,vision,audio] @ git+https://github.com/swiss-ai/transformers.git@0c5e12f18a5ff5c93cb1ef755aebbd557e26e884"

vLLM

from vllm import LLM, SamplingParams

llm = LLM(model="swiss-ai/Apertus-v1.5-70B-base", tensor_parallel_size=4)
outputs = llm.generate(["The capital of Switzerland is"], SamplingParams(max_tokens=64))
print(outputs[0].outputs[0].text)

Transformers

import torch
from transformers import AutoModelForMultimodalLM, AutoProcessor

MODEL_ID = "swiss-ai/Apertus-v1.5-70B-base"

processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(MODEL_ID, dtype="auto", device_map="auto")

inputs = processor(text="The capital of Switzerland is", return_tensors="pt").to(model.device)
with torch.inference_mode():
    output_ids = model.generate(**inputs, max_new_tokens=64)
print(processor.decode(output_ids[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

For image or audio input, add an <|image|> or <|audio|> placeholder to the text and pass images=[...] or audio=[...] to the processor.

Evaluations

Benchmark	Apertus 1.0 (70B)	Apertus 1.5 (70B)	OLMo 3 (32B)	EuroLLM (22B)	Marin (32B)	K2-V2 (70B)	Qwen3.5 (27B)	Nemotron 3.5 (30B-A3B)
General language understanding
HellaSwag	83.0	82.2	81.5	79.0	85.0	83.7	83.4	63.7
PIQA	83.0	82.3	81.3	81.4	83.3	83.9	82.3	83.1
CommonsenseQA	53.8	66.8	71.1	60.5	75.5	73.3	87.4	77.7
LAMBADA	72.9	72.9	70.5	70.9	68.7	76.1	74.2	72.8
XNLI	45.9	46.3	42.4	44.1	42.3	44.1	45.3	43.4
XCOPA	69.9	68.0	59.5	61.3	58.6	63.8	67.3	62.1
Avg	68.1	69.8	67.7	66.2	68.9	70.8	73.3	67.1
Factual knowledge
MMLU CoT	61.8	67.0	71.4	62.9	71.6	70.5	84.9	75.4
Global-MMLU	56.3	59.3	49.1	54.9	54.2	56.1	78.6	69.4
MMLU-Pro	36.0	43.8	48.0	36.8	45.3	45.4	78.0	67.2
AGIEval	41.2	43.9	46.6	40.1	43.9	40.4	62.7	50.5
SQuADv2	18.8	19.1	39.1	35.6	30.1	22.8	43.5	30.1
TruthfulQA	52.5	50.8	54.9	49.3	53.3	54.9	58.3	55.5
Avg	44.4	47.3	51.5	46.6	49.7	48.4	67.7	58.0
Reasoning, mathematics and code
BBH	58.5	68.2	78.8	59.4	71.4	69.3	91.7	77.9
ACP	32.8	32.0	41.7	34.4	40.6	41.6	77.0	47.1
GSM8K	54.0	70.7	80.9	55.8	69.8	68.6	87.3	85.4
GSM8K-P	58.0	72.9	82.7	59.4	70.6	70.1	91.2	88.2
Minerva	17.6	32.7	46.1	24.8	38.1	28.9	72.0	–
MGSM-En	21.6	49.2	51.2	46.4	37.8	36.7	69.0	77.4
HumanEval	55.9	84.6	92.3	66.8	78.4	79.6	98.7	81.6
MBPP	42.4	56.4	66.6	43.8	55.8	59.0	75.8	74.4
Avg	42.6	58.3	67.5	48.9	57.8	56.7	82.8	76.0
Multilingual
XNLI	45.9	46.3	42.4	44.1	42.3	44.1	45.3	43.4
XCOPA	69.9	68.0	59.5	61.3	58.6	63.8	67.3	62.1
ARC-ml	39.6	40.2	34.0	35.9	29.1	34.7	43.6	39.4
HellaSwag-ml	57.6	57.1	47.2	52.9	45.2	52.4	57.3	41.8
LAMBADA-ml	54.9	53.6	48.4	55.7	48.1	50.7	56.7	52.7
TruthfulQA-ml	48.4	48.8	46.7	46.1	47.2	48.3	51.1	48.8
Avg	52.7	52.3	46.4	49.3	45.1	49.0	53.6	48.0
Bias, safety and cultural knowledge
BBQ	55.1	58.6	61.8	57.4	61.2	62.7	63.0	59.5
ToxiGen	44.1	47.2	61.9	39.7	50.3	43.2	58.6	45.5
WMDP	53.3	55.9	60.5	52.5	59.5	59.2	70.0	63.6
CulturalBench	69.9	72.6	74.7	59.5	77.2	75.5	83.7	64.3
Avg	55.6	58.6	64.7	52.3	62.1	60.2	68.8	58.2

Context	Apertus 1.0 (70B)	Apertus 1.5 (70B)	OLMo 3 (32B)
RULER
8k	79.8	86.8	92.7
16k	78.1	85.5	89.4
32k	71.6	78.8	83.9
64k	57.3	71.9	76.3
128k	–	61.8	–
256k	–	51.0	–
HELMET
8k	48.5	49.6	52.7
16k	47.2	49.0	50.8
32k	44.4	44.2	48.8
64k	37.7	41.1	46.2
128k	–	37.8	–

Limitations

Apertus can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.

See the Apertus Charter for the alignment principles that govern the responses of the models.

License

Apache 2.0.

Legal and Compliance

By using the Apertus LLM you agree to indemnify, defend, and hold harmless ETH Zurich and EPFL against any third-party claims arising from your use of Apertus LLM.

EU AI Act documentation (Apertus_1_5_EU_Public_Summary.pdf and Apertus_1_5_EU_Code_of_Practice.pdf) is available at https://github.com/swiss-ai/apertus-legal/, alongside the usage policy and privacy policy.

We strongly advise downloading and updating new model weights from this site every six months.

Data protection contacts: llm-privacy-requests@swiss-ai.org and llm-copyright-requests@swiss-ai.org.

Contact

General inquiries: llm-requests@swiss-ai.org, or https://apertus-ai.org/contact/.

Citation

@misc{apertus2026apertus15opentext,
      title={Apertus 1.5: From Open Text Models to Open Multimodal Reasoners}, 
      author={Project Apertus and : and Yixuan Xu and Yassine Turki and Vinko Sabolčec and Valentina Pyatkin and Tommy Chu and Sven Najem-Meyer and Sean F. Carroll and Robert Smith and Raphael Kreft and Oscar Alessandro Tazza and Oleg Lavrovsky and Nicola Irmiger and Matteo Santelmo and Marian Schneider and Juan García Giraldo and Hanna Yukhymenko and Eduard Ďurech and Davit Melikidze and Dani Tamayo and Bettina Messmer and Aryan Ahadinia and Anunay Yadav and Allen Huang and Ahmad Fraij and Alessandro Bombarda and Alex Padula and Alexander Hägele and Alexander Sternfeld and Anna Hedström and Anna Kosovskaia and Arthur Wuhrmann and Arundhati Balasubramaniam and Badr Al Mahouri and Barna Pasztor and Bartosz Szostakiewicz and Bich Ngoc Doan and Bryan Schmid and Burak Sezen and Camille Challier and Clément Charmillot and Dana Shayakhmetova and Daniel Perruchoud and David Brüggemann and David Isztl and Elia Palme and Emma Anastassova and Fabian Bösch and Faruk Zahiragić and Fawzi Roberto Mohamed and Fiona Panicali and Florian Kolly and Flynn Guerra and Francesco Monti and Frederike Lübeck and Gennaro Oliva and Haofeng Yang and Haoliang Shang and Helen El Khoury and Henrique Mendonça and Jannis Vamvas and Jérome Landolt and Jessica Lam and Jingchun Xiao and Jonathan Coles and Joshua Oyebanji and Khanh Nguyen and Konstantinos Pitas and Lasse B. Strand and Li Mi and Loïc Deslarzes and Lorenzo Baggi and Lorenzo Noci and Luca Baumann and Luca Sartori and Lukas Drescher and Marco De Negri and Marco Passerini and Mark Klein and Marko Stojanovic and Martin Majsec and Matin Ansaripour and Mattea Busato and Mauro Pellonara and Max Neuwinger and Michael Graber and Michal Tešnar and Michelle Wastl and Miguel Gila and Mikolaj Boronski and Morgane Magnin and Moritz Reihs and Mukhammadali Sayfiddinov and Nathan Ranchin and Nicolas Cuveillier and Oscar Wohlfahrt and Qianqian Zou and Rada Kamysheva and Rita Sevastjanova and Roman Machacek and Sebastian Keller and Simon Matrenok and Simon Pierre and Stefano Schuppli and Suei-Wen Chen and Szabina Horváth-Mikulas and Taliesin Perez and Thilo Spinner and Thomas Kleine Buening and Tim Robinson and Tymoteusz Kwiecinski and Vilém Zouhar and Vinzenz Thoma and Xiaozhe Yao and Yannick Metz and Yuhei Fukuhara and Yusif Askari and Zixuan Chen and Alexander Ilic and Ana Klimovic and Andreas Krause and Andrei Kucharavy and Elliott Ash and Joost VandeVondele and Martin Rajman and Mennatallah El-Assady and Thomas Schulthess and Timo Kehrer and Torsten Hoefler and Antoine Bosselut and Martin Jaggi and Imanol Schlag},
      year={2026},
      eprint={2610.15542},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2610.15542}, 
}