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Apollo-1-8B-i1-GGUF/README.md
ModelHub XC feb8f7dce3 初始化项目，由ModelHub XC社区提供模型 
Model: mradermacher/Apollo-1-8B-i1-GGUF
Source: Original Platform
2026-05-09 05:59:52 +08:00

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---
base_model: Loom-Labs/Apollo-1-8B
language:
- en
- fr
- pt
- de
- ro
- sv
- da
- bg
- ru
- cs
- el
- uk
- es
- nl
- sk
- hr
- pl
- lt
- nb
- nn
- fa
- sl
- gu
- lv
- it
- oc
- ne
- mr
- be
- sr
- lb
- vec
- as
- cy
- szl
- ast
- hne
- awa
- mai
- bho
- sd
- ga
- fo
- hi
- pa
- bn
- or
- tg
- yi
- lmo
- lij
- scn
- fur
- sc
- gl
- ca
- is
- sq
- li
- prs
- af
- mk
- si
- ur
- mag
- bs
- hy
- zh
- yue
- my
- ar
- he
- mt
- id
- ms
- tl
- ceb
- jv
- su
- min
- ban
- pag
- ilo
- war
- ta
- te
- kn
- ml
- tr
- az
- uz
- kk
- ba
- tt
- th
- lo
- fi
- et
- hu
- vi
- km
- ja
- ko
- ka
- eu
- ht
- pap
- kea
- tpi
- sw
library_name: transformers
license: other
license_link: https://huggingface.co/apexion-ai/Nous-V1-8B/blob/main/LICENSE.md
license_name: anvdl-1.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
---
## 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/Loom-Labs/Apollo-1-8B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Apollo-1-8B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Apollo-1-8B-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](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.2 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.9 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.9 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-Q4_1.gguf) | i1-Q4_1 | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF/resolve/main/Apollo-1-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.8 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/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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