Apertus is a 70B and 8B parameter language model designed to push the boundaries of fully-open multilingual and transparent models. The model supports over 1000 languages and long context, it uses only fully compliant and open training data, and achieves comparable performance to models trained behind closed doors.

The model is a decoder-only transformer, pretrained on 15T tokens with a staged curriculum of web, code and math data. The model uses a new xIELU activation function and is trained from scratch with the AdEMAMix optimizer. Post-training included supervised fine-tuning and alignment via QRPO.

Key features

Fully open model: open weights + open data + full training details including all data and training recipes
Massively Multilingual: 1811 natively supported languages
Compliant Apertus is trained while respecting opt-out consent of data owners (even retrospectivey), and avoiding memorization of training data

For more details refer to our technical report

How to use

The modeling code for Apertus is available in transformers v4.56.0 and later, so make sure to upgrade your transformers version. You can also load the model with the latest vLLM which uses transformers as a backend.

pip install -U transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "swiss-ai/Apertus-70B-2509"
device = "cuda"  # for GPU usage or "cpu" for CPU usage

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
).to(device)

# prepare the model input
prompt = "Give me a brief explanation of gravity in simple terms."
messages_think = [
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages_think,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Generate the output
generated_ids = model.generate(**model_inputs, max_new_tokens=32768)

# Get and decode the output
output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :]
print(tokenizer.decode(output_ids, skip_special_tokens=True))

[!TIP] We recommend setting temperature=0.8 and top_p=0.9 in the sampling parameters.

Long context processing

Apertus by default supports a context length up to 65,536 tokens.

Agentic Usage

Apertus supports tool use

Deployment

Deployment of the models is directly supported by the newest versions of Transformers, vLLM, SGLang, and also for running on-device with MLX,

Evaluation

Pretraining Evaluation: Performance (%) of Apertus models on general language understanding tasks (higher is better) compared to other pretrained models.

Model	Avg	ARC	HellaSwag	WinoGrande	XNLI	XCOPA	PIQA
Fully Open Models
Apertus-8B	65.8	72.7	59.8	70.6	45.2	66.5	79.8
Apertus-70B	67.5	70.6	64.0	73.3	45.3	69.8	81.9
OLMo2-7B	64.0	72.9	60.4	74.5	40.4	55.2	80.9
OLMo2-32B	67.7	76.2	66.7	78.6	42.9	60.1	82.1
EuroLLM-1.7B	54.8	57.2	44.9	58.1	40.7	55.7	72.4
EuroLLM-9B	62.8	67.9	57.9	68.8	41.5	61.1	79.6
SmolLM2-1.7B	58.5	66.1	52.4	65.6	37.6	52.3	77.0
SmolLM3-3B	61.6	68.6	56.4	68.1	40.5	58.2	77.7
Poro-34B	61.7	65.7	57.9	70.6	41.6	56.0	78.5
Open-Weight Models
Llama3.1-8B	65.4	71.6	60.0	73.4	45.3	61.8	80.1
Llama3.1-70B	67.3	74.4	56.5	79.4	44.3	66.7	82.3
Qwen2.5-7B	64.4	69.6	60.1	72.8	43.3	61.7	78.7
Qwen2.5-72B	69.8	76.2	67.5	78.0	46.9	68.2	82.0
Qwen3-32B	67.8	75.6	64.0	73.8	44.4	67.9	80.9
Llama4-Scout-16x17B	67.9	74.7	66.8	73.2	43.5	67.7	81.2
GPT-OSS-20B	58.1	67.0	41.5	66.5	37.4	60.4	75.6

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.pdf

Training

Model

Architecture: Transformer decoder
Pretraining tokens: 15T
Precision: bfloat16

Software & hardware

GPUs: 4096 GH200
Training Framework: Megatron-LM
...

Open resources

All elements used in the training process are made openly available

Training data reconstruction scripts: github.com/swiss-ai/pretrain-data
The training intermediate checkpoints are available on the different branches of this same repository

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.

Legal Aspects

EU AI Act Transparency Documentation and Code of Practice

Data Protection and Copyright Requests

For removal requests of personally identifiable information (PII) or of copyrighted content, please contact the respective dataset owners or us directly

Output Filter for PII

Currently no output filter is provided.
Please check this site regularly for an output filter that can be used on top of the Apertus LLM. The filter reflects data protection deletion requests which have been addressed to us as the developer of the Apertus LLM. It allows you to remove Personal Data contained in the model output. We strongly advise downloading and applying this output filter from this site every six months.

Contact

To contact us, please send an email to llm-requests@swiss-ai.org

Citation

bash

@misc{swissai2025apertus,
  title={{Apertus: Democratizing Open and Compliant LLMs for Global Language Environments}},
  author={Alejandro Hernández-Cano and Alexander Hägele and Allen Hao Huang and Angelika Romanou and Antoni-Joan Solergibert and Barna Pasztor and Bettina Messmer and Dhia Garbaya and Eduard Frank Ďurech and Ido Hakimi and Juan García Giraldo and Mete Ismayilzada and Negar Foroutan and Skander Moalla and Tiancheng Chen and Vinko Sabolčec and Yixuan Xu and Michael Aerni and Badr AlKhamissi and Ines Altemir Marinas and Mohammad Hossein Amani and Matin Ansaripour and Ilia Badanin and Harold Benoit and Emanuela Boros and Nicholas Browning and Fabian Bösch and Maximilian Böther and Niklas Canova and Camille Challier and Clement Charmillot and Jonathan Coles and Jan Deriu and Arnout Devos and Lukas Drescher and Daniil Dzenhaliou and Maud Ehrmann and Dongyang Fan and Simin Fan and Silin Gao and Miguel Gila and María Grandury and Diba Hashemi and Alexander Hoyle and Jiaming Jiang and Mark Klein and Andrei Kucharavy and Anastasiia Kucherenko and Frederike Lübeck and Roman Machacek and Theofilos Manitaras and Andreas Marfurt and Kyle Matoba and Simon Matrenok and Henrique Mendoncça and Fawzi Roberto Mohamed and Syrielle Montariol and Luca Mouchel and Sven Najem-Meyer and Jingwei Ni and Gennaro Oliva and Matteo Pagliardini and Elia Palme and Andrei Panferov and Léo Paoletti and Marco Passerini and Ivan Pavlov and Auguste Poiroux and Kaustubh Ponkshe and Nathan Ranchin and Javi Rando and Mathieu Sauser and Jakhongir Saydaliev and Muhammad Ali Sayfiddinov and Marian Schneider and Stefano Schuppli and Marco Scialanga and Andrei Semenov and Kumar Shridhar and Raghav Singhal and Anna Sotnikova and Alexander Sternfeld and Ayush Kumar Tarun and Paul Teiletche and Jannis Vamvas and Xiaozhe Yao and Hao Zhao Alexander Ilic and Ana Klimovic and Andreas Krause and Caglar Gulcehre and David Rosenthal and Elliott Ash and Florian Tramèr and Joost VandeVondele and Livio Veraldi and Martin Rajman and Thomas Schulthess and Torsten Hoefler and Antoine Bosselut and Martin Jaggi and Imanol Schlag},
  year={2025},
  howpublished={\url{https://arxiv.org/abs/2509.14233}}
}

from-our-page/Apertus-70B-2509-GGUF

Apertus

Table of Contents

Model Summary