---
base_model: EVA-UNIT-01/EVA-Qwen2.5-32B-v0.1
datasets:
- anthracite-org/kalo-opus-instruct-22k-no-refusal
- Nopm/Opus_WritingStruct
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- Gryphe/Sonnet3.5-Charcard-Roleplay
- Gryphe/ChatGPT-4o-Writing-Prompts
- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
- nothingiisreal/Reddit-Dirty-And-WritingPrompts
- allura-org/Celeste-1.x-data-mixture
- cognitivecomputations/dolphin-2.9.3
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About

<!-- ### quantize_version: 2 -->
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weighted/imatrix quants of https://huggingface.co./EVA-UNIT-01/EVA-Qwen2.5-32B-v0.1

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static quants are available at https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-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/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 |  |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 |  |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 |  |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-IQ2_M.gguf) | i1-IQ2_M | 11.4 |  |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 |  |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-IQ3_M.gguf) | i1-IQ3_M | 14.9 |  |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 |  |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 |  |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.4 |  |
| [GGUF](https://huggingface.co./mradermacher/EVA-Qwen2.5-32B-v0.1-i1-GGUF/resolve/main/EVA-Qwen2.5-32B-v0.1.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | practically like static Q6_K |

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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