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mradermacher/Apertus-v1.1-1.5B-GGUF overview

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…

transformersggufmultilingualcompliantswiss-aiapertusenbase_model:swiss-ai/Apertus-v1.1-1.5Bbase_model:quantized:swiss-ai/Apertus-v1.1-1.5Blicense:apache-2.0endpoints_compatibleregion:us

Runs locally from ~655.5 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

12 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Apertus-v1.1-1.5B.IQ4_XS.ggufGGUFGGUF856.8 MBDownload
Apertus-v1.1-1.5B.Q2_K.ggufGGUFGGUF655.5 MBDownload
Apertus-v1.1-1.5B.Q3_K_L.ggufGGUFGGUF846.1 MBDownload
Apertus-v1.1-1.5B.Q3_K_M.ggufGGUFGGUF791.4 MBDownload
Apertus-v1.1-1.5B.Q3_K_S.ggufGGUFGGUF726.5 MBDownload
Apertus-v1.1-1.5B.Q4_K_M.ggufGGUFGGUF935.3 MBDownload
Apertus-v1.1-1.5B.Q4_K_S.ggufGGUFGGUF890.3 MBDownload
Apertus-v1.1-1.5B.Q5_K_M.ggufGGUFGGUF1.03 GBDownload
Apertus-v1.1-1.5B.Q5_K_S.ggufGGUFGGUF1.01 GBDownload
Apertus-v1.1-1.5B.Q6_K.ggufGGUFGGUF1.16 GBDownload
Apertus-v1.1-1.5B.Q8_0.ggufGGUFGGUF1.50 GBDownload
Apertus-v1.1-1.5B.f16.ggufGGUFGGUF2.82 GBDownload

Model Details

Model IDmradermacher/Apertus-v1.1-1.5B-GGUF
Authormradermacher
Pipeline
Licenseapache-2.0
Base modelswiss-ai/Apertus-v1.1-1.5B
Last modified2026-06-23T01:32:12.000Z

Model README

---

base_model: swiss-ai/Apertus-v1.1-1.5B

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-v1.1-1.5B

<!-- provided-files -->

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Apertus-v1.1-1.5B-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 | Q2_K | 0.8 | |

| GGUF | Q3_K_S | 0.9 | |

| GGUF | Q3_K_M | 0.9 | lower quality |

| GGUF | Q3_K_L | 1.0 | |

| GGUF | IQ4_XS | 1.0 | |

| GGUF | Q4_K_S | 1.0 | fast, recommended |

| GGUF | Q4_K_M | 1.1 | fast, recommended |

| GGUF | Q5_K_S | 1.2 | |

| GGUF | Q5_K_M | 1.2 | |

| GGUF | Q6_K | 1.3 | very good quality |

| GGUF | Q8_0 | 1.7 | fast, best quality |

| GGUF | f16 | 3.1 | 16 bpw, overkill |

Here is a handy graph by ikawrakow comparing some lower-quality quant

types (lower is better):

!image.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, for letting

me use its servers and providing upgrades to my workstation to enable

this work in my free time.

<!-- end -->

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