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Model: mradermacher/Marco-Mini-Instruct-GGUF
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---
base_model: AIDC-AI/Marco-Mini-Instruct
datasets:
- allenai/Dolci-Instruct-SFT
- nvidia/Nemotron-Cascade-2-SFT-Data
- nvidia/Nemotron-RL-instruction_following
- nvidia/Nemotron-RL-instruction_following-structured_outputs
- nvidia/Nemotron-RL-ReasoningGym-v1
- nvidia/Nemotron-RL-knowledge-mcqa
- nvidia/Nemotron-Cascade-RL-RLHF
- BytedTsinghua-SIA/DAPO-Math-17k
- Skywork/Skywork-OR1-RL-Data
- nvidia/Nemotron-SFT-Multilingual-v1
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
- on-policy distillation
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### 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 -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/AIDC-AI/Marco-Mini-Instruct
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Marco-Mini-Instruct-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Marco-Mini-Instruct-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-Mini-Instruct-GGUF/resolve/main/Marco-Mini-Instruct.Q2_K.gguf) | Q2_K | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Instruct-GGUF/resolve/main/Marco-Mini-Instruct.Q3_K_S.gguf) | Q3_K_S | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Instruct-GGUF/resolve/main/Marco-Mini-Instruct.Q3_K_M.gguf) | Q3_K_M | 8.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Instruct-GGUF/resolve/main/Marco-Mini-Instruct.Q3_K_L.gguf) | Q3_K_L | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Instruct-GGUF/resolve/main/Marco-Mini-Instruct.IQ4_XS.gguf) | IQ4_XS | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Instruct-GGUF/resolve/main/Marco-Mini-Instruct.Q4_K_S.gguf) | Q4_K_S | 10.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Instruct-GGUF/resolve/main/Marco-Mini-Instruct.Q4_K_M.gguf) | Q4_K_M | 10.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Instruct-GGUF/resolve/main/Marco-Mini-Instruct.Q5_K_S.gguf) | Q5_K_S | 12.0 | |
| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Instruct-GGUF/resolve/main/Marco-Mini-Instruct.Q5_K_M.gguf) | Q5_K_M | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Instruct-GGUF/resolve/main/Marco-Mini-Instruct.Q6_K.gguf) | Q6_K | 14.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Marco-Mini-Instruct-GGUF/resolve/main/Marco-Mini-Instruct.Q8_0.gguf) | Q8_0 | 18.5 | fast, best quality |
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.
<!-- end -->