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mradermacher/Ling-3.0-flash-base-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…

transformersggufenbase_model:inclusionAI/Ling-3.0-flash-basebase_model:quantized:inclusionAI/Ling-3.0-flash-baselicense:mitendpoints_compatibleregion:usconversational

Runs locally from ~43.34 GB disk (32 GB+ VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

9 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Ling-3.0-flash-base.IQ4_XS.ggufGGUFGGUF63.82 GBDownload
Ling-3.0-flash-base.Q2_K.ggufGGUFGGUF43.34 GBDownload
Ling-3.0-flash-base.Q3_K_L.ggufGGUFGGUF61.49 GBDownload
Ling-3.0-flash-base.Q3_K_M.ggufGGUFGGUF56.58 GBDownload
Ling-3.0-flash-base.Q3_K_S.ggufGGUFGGUF51.36 GBDownload
Ling-3.0-flash-base.Q4_K_M.ggufGGUFGGUF71.72 GBDownload
Ling-3.0-flash-base.Q4_K_S.ggufGGUFGGUF67.43 GBDownload
Ling-3.0-flash-base.Q5_K_M.ggufGGUFGGUF84.25 GBDownload
Ling-3.0-flash-base.Q5_K_S.ggufGGUFGGUF81.86 GBDownload

Model Details

Model IDmradermacher/Ling-3.0-flash-base-GGUF
Authormradermacher
Pipeline
Licensemit
Base modelinclusionAI/Ling-3.0-flash-base
Last modified2026-08-23T21:18:05.000Z

Model README

---

base_model: inclusionAI/Ling-3.0-flash-base

language:

  • en

library_name: transformers

license: mit

mradermacher:

readme_rev: 1

quantized_by: mradermacher

---

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/inclusionAI/Ling-3.0-flash-base

<!-- provided-files -->

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

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Ling-3.0-flash-base-i1-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 | Q2_K | 46.6 | |

| GGUF | Q3_K_S | 55.2 | |

| GGUF | Q3_K_M | 60.9 | lower quality |

| GGUF | Q3_K_L | 66.1 | |

| GGUF | IQ4_XS | 68.6 | |

| GGUF | Q4_K_S | 72.5 | fast, recommended |

| GGUF | Q4_K_M | 77.1 | fast, recommended |

| GGUF | Q5_K_S | 88.0 | |

| GGUF | Q5_K_M | 90.6 | |

| PART 1 PART 2 PART 3 | Q6_K | 104.9 | very good quality |

| PART 1 PART 2 PART 3 | Q8_0 | 135.7 | fast, best quality |

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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