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. Post-training included supervised fine-tuning (SFT) and alignment similar to that of the original Apertus.
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.
Hardware Optimized: Specifically optimized for memory-limited scenarios like mobile and edge deployments, with quantized checkpoints available for Apple devices (MLX) in INT2, INT3, INT4, and INT6 formats.
Quantized Checkpoints
This model family includes base pre-trained models and instruction-tuned models.
For instruction-tuned models, we additionally provide high-quality quantization-aware distillation (QAD) checkpoints, obtained via the official qat-suite. We provide FP8 and NVFP4A16 checkpoints with vLLM inference in mind and INT3-6 checkpoints optimized for mobile usage on Apple devices.
The full list of released checkpoints is shown below:
The modeling code for this Apertus MLX quantization is available via mlx-lm.
pip install mlx-lm
python
1from mlx_lm import load, generate
23model_name ="swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT4"45# load the model and tokenizer6model, tokenizer = load(model_name)78# prepare the model input9prompt ="Give me a brief explanation of gravity in simple terms."10messages_think =[11{"role":"user","content": prompt}12]1314text = tokenizer.apply_chat_template(15 messages_think,16 tokenize=False,17 add_generation_prompt=True,18)1920# Generate the output21response = generate(model, tokenizer, prompt=text, verbose=True, max_tokens=32768)
[!TIP]
We recommend setting temperature=0.8 and top_p=0.9 in the sampling parameters.
Evaluation
Post-Training Multilingual Evaluation: Performance of the Apertus-v1.1 models across multilingual benchmarks compared to models in similar size classes.
Model
Average
MMLU
TruthfulQA
Arc
IF
LogiQA
Apertus-v1.1-0.5B-Instruct
0.318
0.258
0.461
0.225
0.328
0.279
Apertus-v1.1-1.5B-Instruct
0.382
0.377
0.451
0.266
0.434
0.276
Apertus-v1.1-4B-Instruct
0.473
0.504
0.506
0.332
0.550
0.296
Apertus-8B-Instruct-2509
0.534
0.553
0.524
0.368
0.689
0.290
EuroLLM-1.7B-Instruct
0.291
0.260
0.433
0.250
0.222
0.269
EuroLLM-9B-Instruct
0.480
0.520
0.465
0.322
0.613
0.345
gemma-3-270m-it
0.289
0.242
0.465
0.215
0.236
0.205
gemma-3-1b-it
0.406
0.409
0.457
0.250
0.509
0.379
gemma-3-4b-it
0.497
0.547
0.492
0.316
0.635
0.411
SmolLM2-1.7B-Instruct
0.348
0.365
0.452
0.213
0.364
0.246
SmolLM3-3B
0.479
0.507
0.500
0.270
0.637
0.365
Qwen3-0.6B
0.401
0.377
0.464
0.222
0.541
0.353
Qwen3-1.7B
0.457
0.477
0.490
0.251
0.611
0.414
Qwen3-4B
0.521
0.581
0.497
0.274
0.733
0.500
While Apertus-v1.1 demonstrates competitive baseline multilingual chatting performance, it may lack in specific capabilities such as advanced math and complex instruction following.
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}
