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mradermacher/Hypernova-60B-2605-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…

transformersggufenbase_model:MultiverseComputingCAI/Hypernova-60B-2605base_model:quantized:MultiverseComputingCAI/Hypernova-60B-2605license:apache-2.0endpoints_compatibleregion:usimatrixconversational

Runs locally from ~83.2 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

23 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Hypernova-60B-2605.i1-IQ1_M.ggufGGUFIQ1_M25.20 GBDownload
Hypernova-60B-2605.i1-IQ1_S.ggufGGUFIQ1_S24.84 GBDownload
Hypernova-60B-2605.i1-IQ2_M.ggufGGUFIQ2_M27.04 GBDownload
Hypernova-60B-2605.i1-IQ2_S.ggufGGUFIQ2_S26.56 GBDownload
Hypernova-60B-2605.i1-IQ2_XS.ggufGGUFIQ2_XS26.29 GBDownload
Hypernova-60B-2605.i1-IQ2_XXS.ggufGGUFIQ2_XXS25.80 GBDownload
Hypernova-60B-2605.i1-IQ3_M.ggufGGUFIQ3_M29.06 GBDownload
Hypernova-60B-2605.i1-IQ3_S.ggufGGUFIQ3_S28.73 GBDownload
Hypernova-60B-2605.i1-IQ3_XS.ggufGGUFIQ3_XS28.73 GBDownload
Hypernova-60B-2605.i1-IQ3_XXS.ggufGGUFIQ3_XXS27.90 GBDownload
Hypernova-60B-2605.i1-IQ4_XS.ggufGGUFIQ4_XS30.55 GBDownload
Hypernova-60B-2605.i1-Q2_K.ggufGGUFQ2_K28.73 GBDownload
Hypernova-60B-2605.i1-Q2_K_S.ggufGGUFQ2_K_S27.42 GBDownload
Hypernova-60B-2605.i1-Q3_K_L.ggufGGUFQ3_K_L33.35 GBDownload
Hypernova-60B-2605.i1-Q3_K_M.ggufGGUFQ3_K_M31.25 GBDownload
Hypernova-60B-2605.i1-Q3_K_S.ggufGGUFQ3_K_S28.72 GBDownload
Hypernova-60B-2605.i1-Q4_0.ggufGGUFQ4_031.24 GBDownload
Hypernova-60B-2605.i1-Q4_1.ggufGGUFQ4_134.48 GBDownload
Hypernova-60B-2605.i1-Q4_K_M.ggufGGUFQ4_K_M37.89 GBDownload
Hypernova-60B-2605.i1-Q4_K_S.ggufGGUFQ4_K_S35.89 GBDownload
Hypernova-60B-2605.i1-Q5_K_M.ggufGGUFQ5_K_M41.29 GBDownload
Hypernova-60B-2605.i1-Q5_K_S.ggufGGUFQ5_K_S40.12 GBDownload
Hypernova-60B-2605.imatrix.ggufGGUFGGUF83.2 MBDownload

Model Details

Model IDmradermacher/Hypernova-60B-2605-i1-GGUF
Authormradermacher
Pipeline
Licenseapache-2.0
Base modelMultiverseComputingCAI/Hypernova-60B-2605
Last modified2026-06-23T14:18:46.000Z

Model README

---

base_model: MultiverseComputingCAI/Hypernova-60B-2605

language:

  • en

library_name: transformers

license: apache-2.0

mradermacher:

readme_rev: 1

quantized_by: mradermacher

---

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: -->

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

weighted/imatrix quants of https://huggingface.co/MultiverseComputingCAI/Hypernova-60B-2605

<!-- 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/Hypernova-60B-2605-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 | imatrix | 0.2 | imatrix file (for creating your own quants) |

| GGUF | i1-IQ1_S | 26.8 | for the desperate |

| GGUF | i1-IQ1_M | 27.2 | mostly desperate |

| GGUF | i1-IQ2_XXS | 27.8 | |

| GGUF | i1-IQ2_XS | 28.3 | |

| GGUF | i1-IQ2_S | 28.6 | |

| GGUF | i1-IQ2_M | 29.1 | |

| GGUF | i1-Q2_K_S | 29.5 | very low quality |

| GGUF | i1-IQ3_XXS | 30.1 | lower quality |

| GGUF | i1-Q3_K_S | 30.9 | IQ3_XS probably better |

| GGUF | i1-IQ3_S | 30.9 | beats Q3_K* |

| GGUF | i1-IQ3_XS | 30.9 | |

| GGUF | i1-Q2_K | 30.9 | IQ3_XXS probably better |

| GGUF | i1-IQ3_M | 31.3 | |

| GGUF | i1-IQ4_XS | 32.9 | |

| GGUF | i1-Q4_0 | 33.6 | fast, low quality |

| GGUF | i1-Q3_K_M | 33.6 | IQ3_S probably better |

| GGUF | i1-Q3_K_L | 35.9 | IQ3_M probably better |

| GGUF | i1-Q4_1 | 37.1 | |

| GGUF | i1-Q4_K_S | 38.6 | optimal size/speed/quality |

| GGUF | i1-Q4_K_M | 40.8 | fast, recommended |

| GGUF | i1-Q5_K_S | 43.2 | |

| GGUF | i1-Q5_K_M | 44.4 | |

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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