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.
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
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "swiss-ai/Apertus-8B-Instruct-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", add_special_tokens=False).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.
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
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.
For removal requests of personally identifiable information (PII) or of copyrighted content, please contact the respective dataset owners or us directly
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.
@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}}
}
Files
11 files · 7.54 GBWeights 7.52 GB (safetensors). Sizes and sha256 from the signed manifest.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("unsloth/Apertus-8B-Instruct-2509-unsloth-bnb-4bit", revision="a5728d93c0af29e6409e4c8440390680886df9d2")
model = AutoModelForCausalLM.from_pretrained("unsloth/Apertus-8B-Instruct-2509-unsloth-bnb-4bit", revision="a5728d93c0af29e6409e4c8440390680886df9d2", device_map="auto")
unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.textSubject: 
AIBrix
SGLang
Transformers
Axolotl
OLMo-core
Unsloth
Olive
simple-evals
Agent Development Kit
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