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Model: mradermacher/Apertus-v1.1-0.5B-Instruct-GGUF
Source: Original Platform

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README.md

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+---
+base_model: daslab-testing/Apertus-v1.1-0.5B-Instruct
+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/daslab-testing/Apertus-v1.1-0.5B-Instruct
+
+<!-- provided-files -->
+
+***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Apertus-v1.1-0.5B-Instruct-GGUF).***
+
+weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
+## 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-v1.1-0.5B-Instruct-GGUF/resolve/main/Apertus-v1.1-0.5B-Instruct.Q2_K.gguf) | Q2_K | 0.4 |  |
+| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-0.5B-Instruct-GGUF/resolve/main/Apertus-v1.1-0.5B-Instruct.Q3_K_S.gguf) | Q3_K_S | 0.4 |  |
+| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-0.5B-Instruct-GGUF/resolve/main/Apertus-v1.1-0.5B-Instruct.Q3_K_M.gguf) | Q3_K_M | 0.4 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-0.5B-Instruct-GGUF/resolve/main/Apertus-v1.1-0.5B-Instruct.Q3_K_L.gguf) | Q3_K_L | 0.4 |  |
+| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-0.5B-Instruct-GGUF/resolve/main/Apertus-v1.1-0.5B-Instruct.IQ4_XS.gguf) | IQ4_XS | 0.5 |  |
+| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-0.5B-Instruct-GGUF/resolve/main/Apertus-v1.1-0.5B-Instruct.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-0.5B-Instruct-GGUF/resolve/main/Apertus-v1.1-0.5B-Instruct.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-0.5B-Instruct-GGUF/resolve/main/Apertus-v1.1-0.5B-Instruct.Q5_K_S.gguf) | Q5_K_S | 0.5 |  |
+| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-0.5B-Instruct-GGUF/resolve/main/Apertus-v1.1-0.5B-Instruct.Q5_K_M.gguf) | Q5_K_M | 0.5 |  |
+| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-0.5B-Instruct-GGUF/resolve/main/Apertus-v1.1-0.5B-Instruct.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
+| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-0.5B-Instruct-GGUF/resolve/main/Apertus-v1.1-0.5B-Instruct.Q8_0.gguf) | Q8_0 | 0.7 | fast, best quality |
+| [GGUF](https://huggingface.co/mradermacher/Apertus-v1.1-0.5B-Instruct-GGUF/resolve/main/Apertus-v1.1-0.5B-Instruct.f16.gguf) | f16 | 1.3 | 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 -->