Apertus-v1.1 is a series of highly efficient, 0.5-4B billion parameter language models designed to extend the fully-open and compliant Apertus ecosystem to highly constrained hardware environments.
The models rely on a dense transformer architecture featuring grouped-query attention and xIELU activations. To achieve high performance with a minimized memory footprint, this model uses tied embeddings and a deeper, thinner architectural design.
Instead of standard pre-training, Apertus-v1.1 models were created using pre-training distillation (PD) from the Apertus-8B-2509 teacher model. They were trained on 1.7T tokens from Phase 5 of the original Apertus data pipeline—the highest quality tier of filtered documents, code, and instruction samples without introducing any new data sources or licenses.
This model is a base model and has not undergone supervised fine-tuning (SFT) or alignment. It is well-suited for further fine-tuning on specific downstream tasks.
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 retrospectively), and avoiding memorization of training data
Cost-Effective Distillation: Trained using a 90%/10% mix of KL-Divergence and label cross-entropy derived from the 8B teacher model, drastically reducing the required compute.
Quantized Checkpoints
This model family includes base pre-trained models and instruction-tuned models. The full list of released checkpoints across the family is shown below:
As this is a base model, it does not support chat templates. You can use it for standard text completion tasks. The modeling code is available in transformers v4.56.0 and later, so make sure to upgrade your transformers version.
pip install -U transformers
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
23model_name ="swiss-ai/Apertus-v1.1-0.5B"4device ="cuda"# for GPU usage or "cpu" for CPU usage56# load the tokenizer and the model7tokenizer = AutoTokenizer.from_pretrained(model_name)8model = AutoModelForCausalLM.from_pretrained(9 model_name,10).to(device)1112# prepare the model input13prompt ="In simple terms, gravity is"14model_inputs = tokenizer([prompt], return_tensors="pt").to(model.device)1516# Generate the output17generated_ids = model.generate(**model_inputs, max_new_tokens=100)1819# Get and decode the output20print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
Evaluation
Pre-Training Multilingual Evaluation: Performance of the base Apertus-v1.1 models across multilingual benchmarks compared to base models in similar size classes.
Model
Avg
ARC
HellaSwag
WinoGrande
XNLI
XCOPA
PIQA
Apertus-v1.1-0.5B
51.79
44.96
40.42
57.06
41.51
55.49
71.27
Apertus-v1.1-1.5B
56.66
52.66
48.31
61.72
42.94
59.76
74.54
Apertus-v1.1-4B
61.53
61.15
53.51
67.48
45.03
63.82
78.18
Apertus-8B
64.96
71.66
59.62
69.30
44.09
65.69
79.38
EuroLLM-1.7B
54.03
50.80
45.01
59.51
40.88
55.76
72.20
SmolLM2-1.7B
58.00
60.23
53.38
66.22
37.57
53.51
77.10
SmolLM-3B-Base
60.88
64.45
56.37
68.43
40.28
58.02
77.75
Qwen3-0.6B-Base
52.23
48.35
41.01
59.20
39.55
54.96
70.29
Qwen3-1.7B-Base
57.51
56.49
49.36
63.38
41.66
58.35
75.79
Qwen3-4B-Base
62.14
64.99
54.56
70.48
43.00
61.82
77.97
Training
Model Architecture
Apertus-v1.1-0.5B
Architecture Type: Dense transformer decoder with grouped-query attention.
Layers: 20.
Model Dimension: 1024.
MLP Dimension: 6144.
Heads (Q/KV): 16/4.
Tied Embeddings: Yes.
Activation Function: xIELU.
Compute / Storage Size: 0.4B/0.4B parameters.
Pre-Training Details
Training Tokens: 1.7T.
Optimizer: AdEMAMix with WSD schedule and weight decay.
Sequence Handling: Documents packed into chunks of 4096 tokens with cross-document attention masked.
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
The Apertus-v1.1 fully reuses the data of the original Apertus release, meaning the original data summary is representative of this release as well.
EU AI Act Transparency Documentation and Code of Practice
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.
1@misc{panferov2026apertusllmfamilyexpansion,
2title={Apertus LLM Family Expansion via Distillation and Quantization},
3author={Andrei Panferov and Davit Melikidze and Martin Jaggi and Dan Alistarh},
4year={2026},
5eprint={2605.29128},
6archivePrefix={arXiv},
7primaryClass={cs.LG},
8url={[https://arxiv.org/abs/2605.29128](https://arxiv.org/abs/2605.29128)},
9}
