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mradermacher/Kimi-K2.7-Code-i1-GGUF overview

About < quantize version: 2 < output tensor quantised: 1 < convert type: hf < vocab type: < tags: nicoboss < quants: Q2 K IQ3 M Q4 K S IQ3 XXS Q3 K M small IQ4…

transformersggufcompressed-tensorsenbase_model:moonshotai/Kimi-K2.7-Codebase_model:quantized:moonshotai/Kimi-K2.7-Codelicense:otherendpoints_compatibleregion:us

Runs locally from ~1.43 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

1 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Kimi-K2.7-Code.imatrix.ggufGGUFGGUF1.43 GBDownload

Model Details

Model IDmradermacher/Kimi-K2.7-Code-i1-GGUF
Authormradermacher
Pipeline
Licenseother
Base modelmoonshotai/Kimi-K2.7-Code
Last modified2026-07-03T18:00:15.000Z

Model README

---

base_model: moonshotai/Kimi-K2.7-Code

language:

  • en

library_name: transformers

license: other

license_name: modified-mit

mradermacher:

readme_rev: 1

quantized_by: mradermacher

tags:

  • compressed-tensors

---

About

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

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

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

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

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

<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->

<!-- ### quants_skip: Q5_K_S Q5_K_M Q6_K Q8_0 -->

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

weighted/imatrix quants of https://huggingface.co/moonshotai/Kimi-K2.7-Code

<!-- provided-files -->

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

static quants are available at https://huggingface.co/mradermacher/Kimi-K2.7-Code-GGUF

This is a vision model - mmproj files (if any) will be in the static repository.

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 | imatrix | 1.6 | imatrix file (for creating your own quants) |

| P1 P2 P3 P4 P5 | i1-IQ1_S | 204.5 | for the desperate |

| P1 P2 P3 P4 P5 | i1-IQ1_M | 228.0 | mostly desperate |

| P1 P2 P3 P4 P5 P6 | i1-IQ2_XXS | 267.2 | |

| P1 P2 P3 P4 P5 P6 P7 | i1-IQ2_XS | 298.7 | |

| P1 P2 P3 P4 P5 P6 P7 | i1-IQ2_S | 301.0 | |

| P1 P2 P3 P4 P5 P6 P7 | i1-IQ2_M | 332.4 | |

| P1 P2 P3 P4 P5 P6 P7 P8 | i1-Q2_K_S | 346.1 | very low quality |

| P1 P2 P3 P4 P5 P6 P7 P8 | i1-Q2_K | 373.0 | IQ3_XXS probably better |

| P1 P2 P3 P4 P5 P6 P7 P8 P9 | i1-IQ3_XXS | 394.3 | lower quality |

| P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 | i1-IQ3_XS | 417.9 | |

| P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 | i1-IQ3_S | 442.2 | beats Q3_K* |

| P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 | i1-Q3_K_S | 442.2 | IQ3_XS probably better |

| P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 | i1-IQ3_M | 447.2 | |

| P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 | i1-Q3_K_M | 489.2 | IQ3_S probably better |

| P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 | i1-Q3_K_L | 530.6 | IQ3_M probably better |

| P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 | i1-IQ4_XS | 546.3 | |

| P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 | i1-Q4_0 | 580.5 | fast, low quality |

| P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 | i1-Q4_K_S | 582.6 | optimal size/speed/quality |

| P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 | i1-Q4_K_M | 620.7 | fast, recommended |

| P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 | i1-Q4_1 | 642.4 | |

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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