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Riyan200324200324/Cybersecurity-LLM-Qwen3-32B-GGUF overview

About < quantize version: 2 < output tensor quantised: 1 < convert type: hf < vocab type: < tags: nicoboss weighted/imatrix quants of https://huggingface.co/Tr…

transformersggufcybersecurityentrbase_model:Trendyol/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUFbase_model:quantized:Trendyol/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUFlicense:apache-2.0endpoints_compatibleregion:usimatrix

Runs locally from ~6.82 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

23 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ1_M.ggufGGUFQ8_07.41 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ1_S.ggufGGUFQ8_06.82 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ2_M.ggufGGUFQ8_010.58 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ2_S.ggufGGUFQ8_09.79 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ2_XS.ggufGGUFQ8_09.27 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ2_XXS.ggufGGUFQ8_08.40 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ3_M.ggufGGUFQ8_013.90 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ3_S.ggufGGUFQ8_013.44 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ3_XS.ggufGGUFQ8_012.76 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ3_XXS.ggufGGUFQ8_011.94 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ4_XS.ggufGGUFQ8_016.48 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q2_K.ggufGGUFQ8_011.50 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q2_K_S.ggufGGUFQ8_010.68 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q3_K_L.ggufGGUFQ8_016.14 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q3_K_M.ggufGGUFQ8_014.87 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q3_K_S.ggufGGUFQ8_013.40 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q4_0.ggufGGUFQ8_017.42 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q4_1.ggufGGUFQ8_019.22 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q4_K_M.ggufGGUFQ8_018.40 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q4_K_S.ggufGGUFQ8_017.48 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q5_K_M.ggufGGUFQ8_021.62 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q5_K_S.ggufGGUFQ8_021.08 GBDownload
Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q6_K.ggufGGUFQ8_025.04 GBDownload

Model Details

Model IDRiyan200324200324/Cybersecurity-LLM-Qwen3-32B-GGUF
AuthorRiyan200324200324
Pipeline—
Licenseapache-2.0
Base modelTrendyol/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF
Last modified2026-09-27T03:15:36.000Z

Model README

---

base_model: Trendyol/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF

language:

  • en
  • tr

library_name: transformers

license: apache-2.0

quantized_by: mradermacher

tags:

  • cybersecurity

---

About

<!-- ### quantize_version: 2 -->

<!-- ### output_tensor_quantised: 1 -->

<!-- ### convert_type: hf -->

<!-- ### vocab_type: -->

<!-- ### tags: nicoboss -->

weighted/imatrix quants of https://huggingface.co/Trendyol/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF

<!-- provided-files -->

static quants are available at https://huggingface.co/mradermacher/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF-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 | i1-IQ1_S | 7.4 | for the desperate |

| GGUF | i1-IQ1_M | 8.1 | mostly desperate |

| GGUF | i1-IQ2_XXS | 9.1 | |

| GGUF | i1-IQ2_XS | 10.1 | |

| GGUF | i1-IQ2_S | 10.6 | |

| GGUF | i1-IQ2_M | 11.5 | |

| GGUF | i1-Q2_K_S | 11.6 | very low quality |

| GGUF | i1-Q2_K | 12.4 | IQ3_XXS probably better |

| GGUF | i1-IQ3_XXS | 12.9 | lower quality |

| GGUF | i1-IQ3_XS | 13.8 | |

| GGUF | i1-Q3_K_S | 14.5 | IQ3_XS probably better |

| GGUF | i1-IQ3_S | 14.5 | beats Q3_K* |

| GGUF | i1-IQ3_M | 15.0 | |

| GGUF | i1-Q3_K_M | 16.1 | IQ3_S probably better |

| GGUF | i1-Q3_K_L | 17.4 | IQ3_M probably better |

| GGUF | i1-IQ4_XS | 17.8 | |

| GGUF | i1-Q4_0 | 18.8 | fast, low quality |

| GGUF | i1-Q4_K_S | 18.9 | optimal size/speed/quality |

| GGUF | i1-Q4_K_M | 19.9 | fast, recommended |

| GGUF | i1-Q4_1 | 20.7 | |

| GGUF | i1-Q5_K_S | 22.7 | |

| GGUF | i1-Q5_K_M | 23.3 | |

| 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

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, for letting

me use its servers and providing upgrades to my workstation to enable

this work in my free time. Additional thanks to @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.

<!-- end -->

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