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mradermacher/Whittle-Next-27B-A3B-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…

transformersggufwhittleqwen4_expqwen3mixture-of-expertsmoehyper-connectionsn-gram-memoryper-layer-embeddingsknowledge-distillationonline-distillationreasoningthinkingcodemathlong-contextllama.cppsafetensorstext-generationconversationalresearchenzh

Runs locally from ~9.63 GB disk (12 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
1,501
Likes
1
Pipeline
text-generation

Repository Files & Downloads

11 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Whittle-Next-27B-A3B.IQ4_XS.ggufGGUFGGUF14.07 GBDownload
Whittle-Next-27B-A3B.Q2_K.ggufGGUFGGUF9.63 GBDownload
Whittle-Next-27B-A3B.Q3_K_L.ggufGGUFGGUF13.31 GBDownload
Whittle-Next-27B-A3B.Q3_K_M.ggufGGUFGGUF12.40 GBDownload
Whittle-Next-27B-A3B.Q3_K_S.ggufGGUFGGUF11.32 GBDownload
Whittle-Next-27B-A3B.Q4_K_M.ggufGGUFGGUF15.64 GBDownload
Whittle-Next-27B-A3B.Q4_K_S.ggufGGUFGGUF14.78 GBDownload
Whittle-Next-27B-A3B.Q5_K_M.ggufGGUFGGUF18.30 GBDownload
Whittle-Next-27B-A3B.Q5_K_S.ggufGGUFGGUF17.79 GBDownload
Whittle-Next-27B-A3B.Q6_K.ggufGGUFGGUF21.17 GBDownload
Whittle-Next-27B-A3B.Q8_0.ggufGGUFGGUF27.33 GBDownload

Model Details

Model IDmradermacher/Whittle-Next-27B-A3B-GGUF
Authormradermacher
Pipelinetext-generation
Licenseapache-2.0
Base modellogic65/Whittle-Next-27B-A3B
Last modified2026-10-12T17:54:13.000Z

Model README

---

base_model: logic65/Whittle-Next-27B-A3B

language:

  • en
  • zh
  • multilingual

library_name: transformers

license: apache-2.0

mradermacher:

readme_rev: 1

quantized_by: mradermacher

tags:

  • whittle
  • qwen4_exp
  • qwen3
  • mixture-of-experts
  • moe
  • hyper-connections
  • n-gram-memory
  • per-layer-embeddings
  • knowledge-distillation
  • online-distillation
  • reasoning
  • thinking
  • code
  • math
  • long-context
  • llama.cpp
  • safetensors
  • text-generation
  • conversational
  • research

---

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/logic65/Whittle-Next-27B-A3B

<!-- 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 | 10.4 | |

| GGUF | Q3_K_S | 12.3 | |

| GGUF | Q3_K_M | 13.4 | lower quality |

| GGUF | Q3_K_L | 14.4 | |

| GGUF | IQ4_XS | 15.2 | |

| GGUF | Q4_K_S | 16.0 | fast, recommended |

| GGUF | Q4_K_M | 16.9 | fast, recommended |

| GGUF | Q5_K_S | 19.2 | |

| GGUF | Q5_K_M | 19.7 | |

| GGUF | Q6_K | 22.8 | very good quality |

| GGUF | Q8_0 | 29.4 | 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. 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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