Open-weight model
Cybersecurity-LLM-Qwen3-32B-GGUF
by I PUTU RIYAN SAPUTRA Riyan200324200324/Cybersecurity-LLM-Qwen3-32B-GGUF
Cybersecurity-LLM-Qwen3-32B-GGUF is an open-weight model from I PUTU RIYAN SAPUTRA, released under Apache License 2.0. Its published files total 351.8 GB.
weighted/imatrix quants of https://huggingface.co/Trendyol/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q80-GGUF static quants are available at https://huggingface.co/mradermacher/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q80-GGUF-GGUF If you are unsure how to use GGUF…
Model Card
By I PUTU RIYAN SAPUTRA, published under apache-2.0, revision c3db72f0d5e6.
weighted/imatrix quants of https://huggingface.co/Trendyol/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q80-GGUF static quants are available at https://huggingface.co/mradermacher/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q80-GGUF-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…
Read I PUTU RIYAN SAPUTRA's full model card
About
weighted/imatrix quants of https://huggingface.co/Trendyol/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF
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 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):
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.
Identity and Version
- Repository
- Riyan200324200324/Cybersecurity-LLM-Qwen3-32B-GGUF
- Publisher
- I PUTU RIYAN SAPUTRA
- Task
- Not stated by the source
- Modality
- Other
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- en, tr
- Revision
- c3db72f0d5e6052c04561ddb78b267e572767aef
- First published
- 2026-09-27
- Last updated
- 2026-09-27
Files and Weights
26 files, 351.8 GB in total. The weights are 23 files totalling 351.8 GB in gguf.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ1_M.gguf | Weights | 8.0 GB | e921ddcc9f66 |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ1_S.gguf | Weights | 7.3 GB | acfdcce558db |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ2_M.gguf | Weights | 11.4 GB | 0f731325879d |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ2_S.gguf | Weights | 10.5 GB | 57c6d045b553 |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ2_XS.gguf | Weights | 10.0 GB | 238a0ed07128 |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ2_XXS.gguf | Weights | 9.0 GB | b5c6be8fda49 |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ3_M.gguf | Weights | 14.9 GB | 2fc0c29c2b2d |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ3_S.gguf | Weights | 14.4 GB | cf628491f8ce |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ3_XS.gguf | Weights | 13.7 GB | 4ca27c743e15 |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ3_XXS.gguf | Weights | 12.8 GB | f4bdf4305706 |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-IQ4_XS.gguf | Weights | 17.7 GB | 94d2fb70d2b2 |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q2_K.gguf | Weights | 12.3 GB | 91416b94f9de |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q2_K_S.gguf | Weights | 11.5 GB | b98d6f042f1d |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q3_K_L.gguf | Weights | 17.3 GB | 3e6ddcdabaaa |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q3_K_M.gguf | Weights | 16.0 GB | d9b84c97d12c |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q3_K_S.gguf | Weights | 14.4 GB | 0e2ad442a7f0 |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q4_0.gguf | Weights | 18.7 GB | ef3e2e2b7237 |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q4_1.gguf | Weights | 20.6 GB | b03c49c418cf |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q4_K_M.gguf | Weights | 19.8 GB | 55f28a257321 |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q4_K_S.gguf | Weights | 18.8 GB | da1ad306f8cd |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q5_K_M.gguf | Weights | 23.2 GB | 1d163a821941 |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q5_K_S.gguf | Weights | 22.6 GB | f7d1a9350d87 |
| Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF.i1-Q6_K.gguf | Weights | 26.9 GB | 44c0a7991a28 |
| README.md | Documentation | 6.7 KB | — |
| imatrix.dat | Other | 15.2 MB | d6ed133d1913 |
| .gitattributes | Repository | 3.8 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 351.8 GB
Released by I PUTU RIYAN SAPUTRA through its official repository on Hugging Face. Read the license.
Built From
- Derived from Trendyol/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF
- Quantized from Trendyol/Trendyol-Cybersecurity-LLM-Qwen3-32B-Q8_0-GGUF
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 351.8 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Cybersecurity-LLM-Qwen3-32B-GGUF
Can I use Cybersecurity-LLM-Qwen3-32B-GGUF commercially?
Yes. Cybersecurity-LLM-Qwen3-32B-GGUF is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.