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Cartik/Sonexa-Assistant-Qwen2.5-GGUF-4bit overview

About < quantize version: 2 < output tensor quantised: 1 < convert type: hf < vocab type: < tags: static quants of https://huggingface.co/Vikhrmodels/Vikhr Qwe…

transformersggufruendataset:Vikhrmodels/GrandMaster-PRO-MAXbase_model:Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instructbase_model:quantized:Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instructlicense:apache-2.0endpoints_compatibleregion:usconversational

Runs locally from ~334.9 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

3 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Vikhr-Qwen-2.5-0.5b-Instruct.IQ4_XS.ggufGGUFGGUF334.9 MBDownload
Vikhr-Qwen-2.5-0.5b-Instruct.Q4_K_M.ggufGGUFGGUF379.1 MBDownload
Vikhr-Qwen-2.5-0.5b-Instruct.Q4_K_S.ggufGGUFGGUF367.4 MBDownload

Model Details

Model IDCartik/Sonexa-Assistant-Qwen2.5-GGUF-4bit
AuthorCartik
Pipeline
Licenseapache-2.0
Base modelVikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct
Last modified2026-07-03T13:19:31.000Z

Model README

---

base_model: Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct

datasets:

  • Vikhrmodels/GrandMaster-PRO-MAX

language:

  • ru
  • en

library_name: transformers

license: apache-2.0

model_name: Vikhr-Qwen-2.5-0.5b-Instruct

mradermacher:

readme_rev: 1

quantized_by: mradermacher

---

About

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

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

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

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

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

static quants of https://huggingface.co/Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct

<!-- provided-files -->

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

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 | Q3_K_S | 0.4 | |

| GGUF | Q2_K | 0.4 | |

| GGUF | IQ4_XS | 0.5 | |

| GGUF | Q3_K_M | 0.5 | lower quality |

| GGUF | Q3_K_L | 0.5 | |

| GGUF | Q4_K_S | 0.5 | fast, recommended |

| GGUF | Q4_K_M | 0.5 | fast, recommended |

| GGUF | Q5_K_S | 0.5 | |

| GGUF | Q5_K_M | 0.5 | |

| GGUF | Q6_K | 0.6 | very good quality |

| GGUF | Q8_0 | 0.6 | fast, best quality |

| GGUF | f16 | 1.1 | 16 bpw, overkill |

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.

<!-- end -->

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