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---
base_model: Lambent/danube2-upscale-1.7
datasets:
- HuggingFaceTB/cosmopedia-100k
- Vezora/Tested-22k-Python-Alpaca
- sordonia/redpajama-sample_from_valid_all
- nampdn-ai/tiny-bridgedict
- teknium/GPTeacher-General-Instruct
- Severian/Internal-Knowledge-Map
- Severian/Internal-Knowledge-Map-StoryWriter-RolePlaying
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Lambent/danube2-upscale-1.7
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/danube2-upscale-1.7-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/danube2-upscale-1.7-GGUF/resolve/main/danube2-upscale-1.7.Q2_K.gguf) | Q2_K | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/danube2-upscale-1.7-GGUF/resolve/main/danube2-upscale-1.7.Q3_K_S.gguf) | Q3_K_S | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/danube2-upscale-1.7-GGUF/resolve/main/danube2-upscale-1.7.Q3_K_M.gguf) | Q3_K_M | 1.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/danube2-upscale-1.7-GGUF/resolve/main/danube2-upscale-1.7.Q3_K_L.gguf) | Q3_K_L | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/danube2-upscale-1.7-GGUF/resolve/main/danube2-upscale-1.7.IQ4_XS.gguf) | IQ4_XS | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/danube2-upscale-1.7-GGUF/resolve/main/danube2-upscale-1.7.Q4_K_S.gguf) | Q4_K_S | 1.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/danube2-upscale-1.7-GGUF/resolve/main/danube2-upscale-1.7.Q4_K_M.gguf) | Q4_K_M | 1.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/danube2-upscale-1.7-GGUF/resolve/main/danube2-upscale-1.7.Q5_K_S.gguf) | Q5_K_S | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/danube2-upscale-1.7-GGUF/resolve/main/danube2-upscale-1.7.Q5_K_M.gguf) | Q5_K_M | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/danube2-upscale-1.7-GGUF/resolve/main/danube2-upscale-1.7.Q6_K.gguf) | Q6_K | 1.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/danube2-upscale-1.7-GGUF/resolve/main/danube2-upscale-1.7.Q8_0.gguf) | Q8_0 | 2.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/danube2-upscale-1.7-GGUF/resolve/main/danube2-upscale-1.7.f16.gguf) | f16 | 4.6 | 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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