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---
base_model: nightmedia/Qwen3.5-4B-Element
language:
- en
- zh
library_name: transformers
license: apache-2.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
- unsloth
- fine tune
- heretic
- abliterated
- uncensored
- creative
- creative writing
- fiction writing
- plot generation
- sub-plot generation
- fiction writing
- story generation
- scene continue
- storytelling
- fiction story
- science fiction
- romance
- all genres
- story
- writing
- vivid prosing
- vivid writing
- fiction
- roleplaying
- bfloat16
- all use cases
- Deckard(qx)
- mxfp8
- mxfp4
- merge
- mergekit
- mlx
---
## 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/nightmedia/Qwen3.5-4B-Element

<!-- provided-files -->

***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3.5-4B-Element-GGUF).***

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3.5-4B-Element-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/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.5 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.mmproj-f16.gguf) | mmproj-f16 | 0.8 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.Q2_K.gguf) | Q2_K | 2.0 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.Q3_K_S.gguf) | Q3_K_S | 2.2 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.Q3_K_M.gguf) | Q3_K_M | 2.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.Q3_K_L.gguf) | Q3_K_L | 2.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.IQ4_XS.gguf) | IQ4_XS | 2.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.Q4_K_S.gguf) | Q4_K_S | 2.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.Q4_K_M.gguf) | Q4_K_M | 2.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.Q5_K_S.gguf) | Q5_K_S | 3.1 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.Q5_K_M.gguf) | Q5_K_M | 3.2 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.Q6_K.gguf) | Q6_K | 3.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.Q8_0.gguf) | Q8_0 | 4.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.5-4B-Element-GGUF/resolve/main/Qwen3.5-4B-Element.f16.gguf) | f16 | 8.5 | 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 -->