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…
Runs locally from ~83.2 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| Hypernova-60B-2605.i1-IQ1_M.gguf | GGUF | IQ1_M | 25.20 GB | Download |
| Hypernova-60B-2605.i1-IQ1_S.gguf | GGUF | IQ1_S | 24.84 GB | Download |
| Hypernova-60B-2605.i1-IQ2_M.gguf | GGUF | IQ2_M | 27.04 GB | Download |
| Hypernova-60B-2605.i1-IQ2_S.gguf | GGUF | IQ2_S | 26.56 GB | Download |
| Hypernova-60B-2605.i1-IQ2_XS.gguf | GGUF | IQ2_XS | 26.29 GB | Download |
| Hypernova-60B-2605.i1-IQ2_XXS.gguf | GGUF | IQ2_XXS | 25.80 GB | Download |
| Hypernova-60B-2605.i1-IQ3_M.gguf | GGUF | IQ3_M | 29.06 GB | Download |
| Hypernova-60B-2605.i1-IQ3_S.gguf | GGUF | IQ3_S | 28.73 GB | Download |
| Hypernova-60B-2605.i1-IQ3_XS.gguf | GGUF | IQ3_XS | 28.73 GB | Download |
| Hypernova-60B-2605.i1-IQ3_XXS.gguf | GGUF | IQ3_XXS | 27.90 GB | Download |
| Hypernova-60B-2605.i1-IQ4_XS.gguf | GGUF | IQ4_XS | 30.55 GB | Download |
| Hypernova-60B-2605.i1-Q2_K.gguf | GGUF | Q2_K | 28.73 GB | Download |
| Hypernova-60B-2605.i1-Q2_K_S.gguf | GGUF | Q2_K_S | 27.42 GB | Download |
| Hypernova-60B-2605.i1-Q3_K_L.gguf | GGUF | Q3_K_L | 33.35 GB | Download |
| Hypernova-60B-2605.i1-Q3_K_M.gguf | GGUF | Q3_K_M | 31.25 GB | Download |
| Hypernova-60B-2605.i1-Q3_K_S.gguf | GGUF | Q3_K_S | 28.72 GB | Download |
| Hypernova-60B-2605.i1-Q4_0.gguf | GGUF | Q4_0 | 31.24 GB | Download |
| Hypernova-60B-2605.i1-Q4_1.gguf | GGUF | Q4_1 | 34.48 GB | Download |
| Hypernova-60B-2605.i1-Q4_K_M.gguf | GGUF | Q4_K_M | 37.89 GB | Download |
| Hypernova-60B-2605.i1-Q4_K_S.gguf | GGUF | Q4_K_S | 35.89 GB | Download |
| Hypernova-60B-2605.i1-Q5_K_M.gguf | GGUF | Q5_K_M | 41.29 GB | Download |
| Hypernova-60B-2605.i1-Q5_K_S.gguf | GGUF | Q5_K_S | 40.12 GB | Download |
| Hypernova-60B-2605.imatrix.gguf | GGUF | GGUF | 83.2 MB | Download |
Model Details
| Model ID | mradermacher/Hypernova-60B-2605-i1-GGUF |
|---|---|
| Author | mradermacher |
| Pipeline | — |
| License | apache-2.0 |
| Base model | MultiverseComputingCAI/Hypernova-60B-2605 |
| Last modified | 2026-06-23T14:18:46.000Z |
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):
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.
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