---
base_model: ATH-MaaS/Marco-Nano-Base
datasets:
- nvidia/Nemotron-CC-v2
- nvidia/Nemotron-Pretraining-SFT-v1
- nvidia/Nemotron-Pretraining-Specialized-v1
- nvidia/Nemotron-CC-v2.1
- allenai/dolmino-mix-1124
- nvidia/Nemotron-CC-Math-v1
- nvidia/OpenMathInstruct-2
- HuggingFaceTB/finemath
- LLM360/MegaMath
- open-thoughts/OpenThoughts3-1.2M
- opencsg/Fineweb-Edu-Chinese-V2.1
- HuggingFaceFW/fineweb-2
- allenai/dolma3_dolmino_mix-100B-1125
language:
- en
- zh
- ar
- de
- es
- fr
- ko
- ja
- pt
- tr
- id
- it
- nl
- pl
- ru
- vi
- th
- he
- uk
- ms
- bn
- cs
- ur
- kk
- el
- ro
- hu
- ne
- az
library_name: transformers
license: apache-2.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
- moe
- mixture-of-experts
- multilingual
- upcycling
---
## About

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<!-- ### 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 -->
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static quants of https://huggingface.co/ATH-MaaS/Marco-Nano-Base

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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Marco-Nano-Base-GGUF).***

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Marco-Nano-Base-i1-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/Marco-Nano-Base-GGUF/resolve/main/Marco-Nano-Base.Q2_K.gguf) | Q2_K | 3.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-GGUF/resolve/main/Marco-Nano-Base.Q3_K_S.gguf) | Q3_K_S | 4.0 |  |
| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-GGUF/resolve/main/Marco-Nano-Base.Q3_K_M.gguf) | Q3_K_M | 4.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-GGUF/resolve/main/Marco-Nano-Base.Q3_K_L.gguf) | Q3_K_L | 4.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-GGUF/resolve/main/Marco-Nano-Base.IQ4_XS.gguf) | IQ4_XS | 4.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-GGUF/resolve/main/Marco-Nano-Base.Q4_K_S.gguf) | Q4_K_S | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-GGUF/resolve/main/Marco-Nano-Base.Q4_K_M.gguf) | Q4_K_M | 5.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-GGUF/resolve/main/Marco-Nano-Base.Q5_K_S.gguf) | Q5_K_S | 5.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-GGUF/resolve/main/Marco-Nano-Base.Q5_K_M.gguf) | Q5_K_M | 6.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-GGUF/resolve/main/Marco-Nano-Base.Q6_K.gguf) | Q6_K | 7.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-GGUF/resolve/main/Marco-Nano-Base.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Marco-Nano-Base-GGUF/resolve/main/Marco-Nano-Base.f16.gguf) | f16 | 16.4 | 16 bpw, overkill |

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.

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