transformers/docs/source/en/model_doc/apertus.md

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*This model was contributed to Hugging Face Transformers on 2025-08-28.*

# Apertus

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
        <img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
    </div>
</div>

## Overview

[Apertus](https://www.swiss-ai.org) is a family of large language models from the Swiss AI Initiative.

> [!TIP]
> Coming soon

The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModel`], and from the command line.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline


pipeline = pipeline(
    task="text-generation",
    model="swiss-ai/Apertus-8B",
    device=0
)
pipeline("Plants create energy through a process known as")
```

</hfoption>
<hfoption id="AutoModel">

```python
from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained(
    "swiss-ai/Apertus-8B",
)
model = AutoModelForCausalLM.from_pretrained(
    "swiss-ai/Apertus-8B",
    device_map="auto",
    attn_implementation="sdpa"
)
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)

output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>
</hfoptions>

## ApertusConfig

[[autodoc]] ApertusConfig

## ApertusModel

[[autodoc]] ApertusModel
    - forward

## ApertusForCausalLM

[[autodoc]] ApertusForCausalLM
    - forward

## ApertusForTokenClassification

[[autodoc]] ApertusForTokenClassification
    - forward