99 lines
5.2 KiB
Markdown
99 lines
5.2 KiB
Markdown
---
|
|
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):
|
|
|
|
|
|
|
|
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 -->
|