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mradermacher/EventTwin-GGUF overview

About < quantize version: 2 < output tensor quantised: 1 < convert type: hf < vocab type: < tags: < quants: x f16 Q4 K S Q2 K Q8 0 Q6 K Q3 K M Q3 K S Q3 K L Q4…

transformersggufcross-encoderrerankerevent-coreferenceevent-deduplicationchinesenews-clusteringknowledge-distillationcalibrated-probabilitieszhbase_model:MaYiding/EventTwinbase_model:quantized:MaYiding/EventTwinlicense:apache-2.0endpoints_compatibleregion:usfeature-extraction

Runs locally from ~349.5 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
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Pipeline
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Repository Files & Downloads

12 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
EventTwin.IQ4_XS.ggufGGUFGGUF395.4 MBDownload
EventTwin.Q2_K.ggufGGUFGGUF349.5 MBDownload
EventTwin.Q3_K_L.ggufGGUFGGUF401.3 MBDownload
EventTwin.Q3_K_M.ggufGGUFGGUF384.1 MBDownload
EventTwin.Q3_K_S.ggufGGUFGGUF364.2 MBDownload
EventTwin.Q4_K_M.ggufGGUFGGUF418.1 MBDownload
EventTwin.Q4_K_S.ggufGGUFGGUF404.6 MBDownload
EventTwin.Q5_K_M.ggufGGUFGGUF446.7 MBDownload
EventTwin.Q5_K_S.ggufGGUFGGUF438.7 MBDownload
EventTwin.Q6_K.ggufGGUFGGUF477.1 MBDownload
EventTwin.Q8_0.ggufGGUFGGUF606.2 MBDownload
EventTwin.f16.ggufGGUFGGUF1.08 GBDownload

Model Details

Model IDmradermacher/EventTwin-GGUF
Authormradermacher
Pipeline—
Licenseapache-2.0
Base modelMaYiding/EventTwin
Last modified2026-10-07T02:59:40.000Z

Model README

---

base_model: MaYiding/EventTwin

language:

  • zh

library_name: transformers

license: apache-2.0

mradermacher:

readme_rev: 1

quantized_by: mradermacher

tags:

  • cross-encoder
  • reranker
  • event-coreference
  • event-deduplication
  • chinese
  • news-clustering
  • knowledge-distillation
  • calibrated-probabilities

---

About

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

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

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

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

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

<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->

<!-- ### quants_skip: -->

<!-- ### skip_mmproj: -->

static quants of https://huggingface.co/MaYiding/EventTwin

<!-- 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 | Q2_K | 0.5 | |

| GGUF | Q3_K_S | 0.5 | |

| GGUF | Q3_K_M | 0.5 | lower quality |

| GGUF | IQ4_XS | 0.5 | |

| 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 | 0.6 | |

| GGUF | Q5_K_M | 0.6 | |

| GGUF | Q6_K | 0.6 | very good quality |

| GGUF | Q8_0 | 0.7 | fast, best quality |

| GGUF | f16 | 1.3 | 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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