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Model: mradermacher/gemma-3n-E4B-it-less-heretic-GGUF
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---
base_model: MuXodious/gemma-3n-E4B-it-less-heretic
extra_gated_button_content: Acknowledge license
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
language:
- en
library_name: transformers
license: gemma
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- automatic-speech-recognition
- automatic-speech-translation
- audio-text-to-text
- video-text-to-text
---
## 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/MuXodious/gemma-3n-E4B-it-less-heretic
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#gemma-3n-E4B-it-less-heretic-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-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](https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-GGUF/resolve/main/gemma-3n-E4B-it-less-heretic.Q2_K.gguf) | Q2_K | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-GGUF/resolve/main/gemma-3n-E4B-it-less-heretic.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-GGUF/resolve/main/gemma-3n-E4B-it-less-heretic.Q3_K_M.gguf) | Q3_K_M | 3.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-GGUF/resolve/main/gemma-3n-E4B-it-less-heretic.Q3_K_L.gguf) | Q3_K_L | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-GGUF/resolve/main/gemma-3n-E4B-it-less-heretic.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-GGUF/resolve/main/gemma-3n-E4B-it-less-heretic.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-GGUF/resolve/main/gemma-3n-E4B-it-less-heretic.Q4_K_M.gguf) | Q4_K_M | 4.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-GGUF/resolve/main/gemma-3n-E4B-it-less-heretic.Q5_K_S.gguf) | Q5_K_S | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-GGUF/resolve/main/gemma-3n-E4B-it-less-heretic.Q5_K_M.gguf) | Q5_K_M | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-GGUF/resolve/main/gemma-3n-E4B-it-less-heretic.Q6_K.gguf) | Q6_K | 5.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-GGUF/resolve/main/gemma-3n-E4B-it-less-heretic.Q8_0.gguf) | Q8_0 | 7.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3n-E4B-it-less-heretic-GGUF/resolve/main/gemma-3n-E4B-it-less-heretic.f16.gguf) | f16 | 13.8 | 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 -->

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