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  <!-- ### quantize_version: 2 -->
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  <!-- ### output_tensor_quantised: 1 -->
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  <!-- ### convert_type: hf -->
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  <!-- ### quants_skip: -->
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  <!-- ### skip_mmproj: -->
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  static quants of https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: swiss-ai/Apertus-8B-Instruct-2509
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+ extra_gated_button_content: Submit
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+ extra_gated_fields:
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+ Affiliation: text
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+ By clicking Submit below I accept the terms of use: checkbox
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+ Country: country
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+ Your Name: text
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+ geo: ip_location
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+ extra_gated_prompt: "### Apertus LLM Acceptable Use Policy \n(1.0 | September 1,
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+ 2025)\n\"Agreement\" The Swiss National AI Institute (SNAI) is a partnership between
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+ the two Swiss Federal Institutes of Technology, ETH Zurich and EPFL. \n\nBy using
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+ the Apertus LLM you agree to indemnify, defend, and hold harmless ETH Zurich and
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+ EPFL against any third-party claims arising from your use of Apertus LLM. \n\nThe
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+ training data and the Apertus LLM may contain or generate information that directly
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+ or indirectly refers to an identifiable individual (Personal Data). You process
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+ Personal Data as independent controller in accordance with applicable data protection
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+ law. SNAI will regularly provide a file with hash values for download which you
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+ can apply as an output filter to your use of our Apertus LLM. The file reflects
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+ data protection deletion requests which have been addressed to SNAI as the developer
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+ of the Apertus LLM. It allows you to remove Personal Data contained in the model
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+ output. We strongly advise downloading and applying this output filter from SNAI
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+ every six months following the release of the model. "
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+ language:
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+ - en
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+ library_name: transformers
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+ license: apache-2.0
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+ mradermacher:
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+ readme_rev: 1
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+ quantized_by: mradermacher
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+ tags:
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+ - multilingual
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+ - compliant
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+ - swiss-ai
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+ - apertus
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+ ---
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+ ## About
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+
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  <!-- ### quantize_version: 2 -->
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  <!-- ### output_tensor_quantised: 1 -->
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  <!-- ### convert_type: hf -->
 
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  <!-- ### quants_skip: -->
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  <!-- ### skip_mmproj: -->
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  static quants of https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509
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+
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+ <!-- provided-files -->
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+
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+ ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Apertus-8B-Instruct-2509-GGUF).***
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+
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+ 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.
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+ ## Usage
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+
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+ If you are unsure how to use GGUF files, refer to one of [TheBloke's
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+ READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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+ more details, including on how to concatenate multi-part files.
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+
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+ ## Provided Quants
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+
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+ (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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+
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+ | Link | Type | Size/GB | Notes |
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+ |:-----|:-----|--------:|:------|
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+ | [GGUF](https://huggingface.co/mradermacher/Apertus-8B-Instruct-2509-GGUF/resolve/main/Apertus-8B-Instruct-2509.Q2_K.gguf) | Q2_K | 3.4 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Apertus-8B-Instruct-2509-GGUF/resolve/main/Apertus-8B-Instruct-2509.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Apertus-8B-Instruct-2509-GGUF/resolve/main/Apertus-8B-Instruct-2509.Q3_K_M.gguf) | Q3_K_M | 4.3 | lower quality |
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+ | [GGUF](https://huggingface.co/mradermacher/Apertus-8B-Instruct-2509-GGUF/resolve/main/Apertus-8B-Instruct-2509.Q3_K_L.gguf) | Q3_K_L | 4.7 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Apertus-8B-Instruct-2509-GGUF/resolve/main/Apertus-8B-Instruct-2509.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
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+ | [GGUF](https://huggingface.co/mradermacher/Apertus-8B-Instruct-2509-GGUF/resolve/main/Apertus-8B-Instruct-2509.Q4_K_M.gguf) | Q4_K_M | 5.2 | fast, recommended |
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+ | [GGUF](https://huggingface.co/mradermacher/Apertus-8B-Instruct-2509-GGUF/resolve/main/Apertus-8B-Instruct-2509.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
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+ | [GGUF](https://huggingface.co/mradermacher/Apertus-8B-Instruct-2509-GGUF/resolve/main/Apertus-8B-Instruct-2509.Q8_0.gguf) | Q8_0 | 8.7 | fast, best quality |
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+ | [GGUF](https://huggingface.co/mradermacher/Apertus-8B-Instruct-2509-GGUF/resolve/main/Apertus-8B-Instruct-2509.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
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+
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+ Here is a handy graph by ikawrakow comparing some lower-quality quant
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+ types (lower is better):
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+
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+ ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
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+
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+ And here are Artefact2's thoughts on the matter:
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+ https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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+
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+ ## FAQ / Model Request
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+
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+ See https://huggingface.co/mradermacher/model_requests for some answers to
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+ questions you might have and/or if you want some other model quantized.
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+
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+ ## Thanks
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+
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+ I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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+ me use its servers and providing upgrades to my workstation to enable
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+ this work in my free time.
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+
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+ <!-- end -->