---
base_model: nbeerbower/Dumpling-Qwen2.5-32B
datasets:
- nbeerbower/GreatFirewall-DPO
- nbeerbower/Schule-DPO
- nbeerbower/Purpura-DPO
- nbeerbower/Arkhaios-DPO
- jondurbin/truthy-dpo-v0.1
- antiven0m/physical-reasoning-dpo
- flammenai/Date-DPO-NoAsterisks
- flammenai/Prude-Phi3-DPO
- Atsunori/HelpSteer2-DPO
- jondurbin/gutenberg-dpo-v0.1
- nbeerbower/gutenberg2-dpo
- nbeerbower/gutenberg-moderne-dpo
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About

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static quants of https://huggingface.co/nbeerbower/Dumpling-Qwen2.5-32B

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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Dumpling-Qwen2.5-32B-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/Dumpling-Qwen2.5-32B-GGUF/resolve/main/Dumpling-Qwen2.5-32B.Q2_K.gguf) | Q2_K | 12.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-32B-GGUF/resolve/main/Dumpling-Qwen2.5-32B.Q3_K_S.gguf) | Q3_K_S | 14.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-32B-GGUF/resolve/main/Dumpling-Qwen2.5-32B.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-32B-GGUF/resolve/main/Dumpling-Qwen2.5-32B.Q3_K_L.gguf) | Q3_K_L | 17.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-32B-GGUF/resolve/main/Dumpling-Qwen2.5-32B.IQ4_XS.gguf) | IQ4_XS | 18.0 |  |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-32B-GGUF/resolve/main/Dumpling-Qwen2.5-32B.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-32B-GGUF/resolve/main/Dumpling-Qwen2.5-32B.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-32B-GGUF/resolve/main/Dumpling-Qwen2.5-32B.Q5_K_S.gguf) | Q5_K_S | 22.7 |  |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-32B-GGUF/resolve/main/Dumpling-Qwen2.5-32B.Q5_K_M.gguf) | Q5_K_M | 23.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-32B-GGUF/resolve/main/Dumpling-Qwen2.5-32B.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-32B-GGUF/resolve/main/Dumpling-Qwen2.5-32B.Q8_0.gguf) | Q8_0 | 34.9 | fast, best quality |

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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

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