Instructions to use daslab-testing/Apertus-v1.1-0.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use daslab-testing/Apertus-v1.1-0.5B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="daslab-testing/Apertus-v1.1-0.5B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("daslab-testing/Apertus-v1.1-0.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("daslab-testing/Apertus-v1.1-0.5B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use daslab-testing/Apertus-v1.1-0.5B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "daslab-testing/Apertus-v1.1-0.5B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "daslab-testing/Apertus-v1.1-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/daslab-testing/Apertus-v1.1-0.5B-Instruct

SGLang

How to use daslab-testing/Apertus-v1.1-0.5B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "daslab-testing/Apertus-v1.1-0.5B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "daslab-testing/Apertus-v1.1-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "daslab-testing/Apertus-v1.1-0.5B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "daslab-testing/Apertus-v1.1-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use daslab-testing/Apertus-v1.1-0.5B-Instruct with Docker Model Runner:
```
docker model run hf.co/daslab-testing/Apertus-v1.1-0.5B-Instruct
```

Apertus-v1.1-0.5B-Instruct / README.md

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metadata

license: apache-2.0
base_model:
  - swiss-ai/Apertus-v1.1-0.5B
pipeline_tag: text-generation
library_name: transformers
tags:
  - multilingual
  - compliant
  - swiss-ai
  - apertus
extra_gated_prompt: >-
  ### Apertus LLM Acceptable Use Policy  

  (1.0 | September 1, 2025)

  "Agreement" The Swiss National AI Institute (SNAI) is a partnership between
  the two Swiss Federal Institutes of Technology, ETH Zurich and EPFL. 


  By using the Apertus LLM you agree to indemnify, defend, and hold harmless ETH
  Zurich and EPFL against any third-party claims arising from your use of
  Apertus LLM. 


  The training data and the Apertus LLM may contain or generate information that
  directly or indirectly refers to an identifiable individual (Personal Data).
  You process Personal Data as independent controller in accordance with
  applicable data protection law. SNAI will regularly provide a file with hash
  values for download which you can apply as an output filter to your use of our
  Apertus LLM. The file reflects data protection deletion requests which have
  been addressed to SNAI 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 SNAI every six months
  following the release of the model.  
extra_gated_fields:
  Your Name: text
  Country: country
  Affiliation: text
  geo: ip_location
  By clicking Submit below I accept the terms of use: checkbox
extra_gated_button_content: Submit

Apertus-v1.1-0.5B-Instruct

Model Summary
How to use
Evaluation
Training
Limitations
Legal Aspects

Model Summary

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:

	BF16	BF16	FP8	NVFP4A16	INT3	INT4	INT6
	Base	Instruct	Instruct	Instruct	Instruct	Instruct	Instruct
0.5B	✅	✅	✅	✅	✅	✅	✅
1.5B	✅	✅	✅	✅	✅	✅	✅
4B	✅	✅	✅	✅	✅	✅	✅
8B	✅	✅	✅	✅	❌	✅	❌

For more details refer to the original Apertus technical report and the new Apertus distillation 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-v1.1-0.5B-Instruct"
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))

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.
Total Compute: 0.2E22 FLOPs.

Software & hardware

GPUs: 64 GH200
Pre-Training Distillation Framework: Megatron-LM
Post-Training Framework: posttraining
Quantization Suite: qat-suite

Open resources

All elements used in the training process are made openly available

Training data reconstruction scripts: github.com/swiss-ai/pretrain-data

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

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

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

@misc{panferov2026apertusllmfamilyexpansion,
      title={Apertus LLM Family Expansion via Distillation and Quantization}, 
      author={Andrei Panferov and Davit Melikidze and Martin Jaggi and Dan Alistarh},
      year={2026},
      eprint={2605.29128},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={[https://arxiv.org/abs/2605.29128](https://arxiv.org/abs/2605.29128)}, 
}

daslab-testing
/

Apertus-v1.1-0.5B-Instruct

Apertus-v1.1-0.5B-Instruct

Table of Contents

Model Summary

Key features

Quantized Checkpoints

How to use

Evaluation

Training

Model Architecture

Pre-Training Details

Software & hardware

Open resources

Limitations

Legal Aspects

EU AI Act Transparency Documentation and Code of Practice

Data Protection and Copyright Requests

Output Filter for PII

Contact

Citation