Apertus-2509

Table of Contents

1. Model Summary
2. How to use
3. Evaluation
4. Training
5. Limitations
6. Legal Aspects

Model Summary

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

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.

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.

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

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

• Apertus_EU_Public_Summary.pdf
• [Apertus_EU_Code_of_Practice.pdf](https://h