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---
base_model: swiss-ai/Apertus-v1.1-4B
extra_gated_button_content: Submit
extra_gated_fields:
Affiliation: text
By clicking Submit below I accept the terms of use: checkbox
Country: country
Your Name: text
geo: ip_location
extra_gated_prompt: "### Apertus LLM Acceptable Use Policy \n(1.0 | September 1,
2025)\n\"Agreement\" The Swiss National AI Institute (SNAI) is a partnership between
the two Swiss Federal Institutes of Technology, ETH Zurich and EPFL. \n\nBy 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. \n\nThe
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. "
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- multilingual
- compliant
- swiss-ai
- apertus
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/swiss-ai/Apertus-v1.1-4B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Apertus-v1.1-4B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Apertus-v1.1-4B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-4B-GGUF/resolve/main/Apertus-v1.1-4B.Q2_K.gguf) | Q2_K | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-4B-GGUF/resolve/main/Apertus-v1.1-4B.Q3_K_S.gguf) | Q3_K_S | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-4B-GGUF/resolve/main/Apertus-v1.1-4B.Q3_K_M.gguf) | Q3_K_M | 2.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-4B-GGUF/resolve/main/Apertus-v1.1-4B.IQ4_XS.gguf) | IQ4_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-4B-GGUF/resolve/main/Apertus-v1.1-4B.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-4B-GGUF/resolve/main/Apertus-v1.1-4B.Q4_K_S.gguf) | Q4_K_S | 2.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-4B-GGUF/resolve/main/Apertus-v1.1-4B.Q4_K_M.gguf) | Q4_K_M | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-4B-GGUF/resolve/main/Apertus-v1.1-4B.Q5_K_S.gguf) | Q5_K_S | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-4B-GGUF/resolve/main/Apertus-v1.1-4B.Q5_K_M.gguf) | Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-4B-GGUF/resolve/main/Apertus-v1.1-4B.Q6_K.gguf) | Q6_K | 3.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-4B-GGUF/resolve/main/Apertus-v1.1-4B.Q8_0.gguf) | Q8_0 | 4.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-4B-GGUF/resolve/main/Apertus-v1.1-4B.f16.gguf) | f16 | 7.8 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->