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---
base_model: AIML-TUDA/QwenGuard-v1.2-3B
datasets: AIML-TUDA/LlavaGuard
extra_gated_fields:
Affiliation: text
Country: text
Email: text
? I have explicitly checked that downloading LlavaGuard is legal in my jurisdiction,
in the country/region where I am located right now, and for the use case that
I have described above, I have also read and accepted the relevant Terms of Use
: checkbox
Name: text
extra_gated_prompt: By filling out the form below I understand that LlavaGuard is
a derivative model based on webscraped images and the SMID dataset that use individual
licenses and their respective terms and conditions apply. I understand that all
content uses are subject to the terms of use. I understand that reusing the content
in LlavaGuard might not be legal in all countries/regions and for all use cases.
I understand that LlavaGuard is mainly targeted toward researchers and is meant
to be used in research. LlavaGuard authors reserve the right to revoke my access
to this data. They reserve the right to modify this data at any time in accordance
with take-down requests.
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- llama-factory
- freeze
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/AIML-TUDA/QwenGuard-v1.2-3B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#QwenGuard-v1.2-3B-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/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.9 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.Q2_K.gguf) | Q2_K | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.mmproj-f16.gguf) | mmproj-f16 | 1.4 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.Q3_K_M.gguf) | Q3_K_M | 1.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.Q3_K_L.gguf) | Q3_K_L | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.Q4_K_S.gguf) | Q4_K_S | 1.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.Q4_K_M.gguf) | Q4_K_M | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.Q5_K_S.gguf) | Q5_K_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.Q5_K_M.gguf) | Q5_K_M | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.Q6_K.gguf) | Q6_K | 2.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.Q8_0.gguf) | Q8_0 | 3.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-3B-GGUF/resolve/main/QwenGuard-v1.2-3B.f16.gguf) | f16 | 6.3 | 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 -->