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---
base_model: trollek/NinjaMouse-2.4B-32L-danube
datasets:
- m-a-p/Code-Feedback
- HuggingFaceTB/cosmopedia-100k
- LDJnr/Capybara
- vicgalle/alpaca-gpt4
- glaiveai/glaive-code-assistant-v2
- WhiteRabbitNeo/WRN-Chapter-1
- WhiteRabbitNeo/WRN-Chapter-2
- m-a-p/CodeFeedback-Filtered-Instruction
- jondurbin/airoboros-3.2
- euclaise/WritingPrompts_curated
- derek-thomas/squad-v1.1-t5-question-generation
- reinforz/question_generation_data
- teknium/GPTeacher-General-Instruct
- dim/roleplay_instruct_v2_final
- TIGER-Lab/MathInstruct
- abacusai/SystemChat
- Mihaiii/OpenHermes-2.5-1k-longest-curated
language:
- en
library_name: transformers
license: apache-2.0
license_link: LICENSE
license_name: a
quantized_by: mradermacher
tags:
- code
---
## About
<!-- ### quantize_version: 2 -->
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<!-- ### convert_type: hf -->
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static quants of https://huggingface.co/trollek/NinjaMouse-2.4B-32L-danube
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/NinjaMouse-2.4B-32L-danube-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/NinjaMouse-2.4B-32L-danube-GGUF/resolve/main/NinjaMouse-2.4B-32L-danube.Q2_K.gguf) | Q2_K | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/NinjaMouse-2.4B-32L-danube-GGUF/resolve/main/NinjaMouse-2.4B-32L-danube.Q3_K_S.gguf) | Q3_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/NinjaMouse-2.4B-32L-danube-GGUF/resolve/main/NinjaMouse-2.4B-32L-danube.Q3_K_M.gguf) | Q3_K_M | 1.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NinjaMouse-2.4B-32L-danube-GGUF/resolve/main/NinjaMouse-2.4B-32L-danube.Q3_K_L.gguf) | Q3_K_L | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/NinjaMouse-2.4B-32L-danube-GGUF/resolve/main/NinjaMouse-2.4B-32L-danube.IQ4_XS.gguf) | IQ4_XS | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/NinjaMouse-2.4B-32L-danube-GGUF/resolve/main/NinjaMouse-2.4B-32L-danube.Q4_K_S.gguf) | Q4_K_S | 1.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NinjaMouse-2.4B-32L-danube-GGUF/resolve/main/NinjaMouse-2.4B-32L-danube.Q4_K_M.gguf) | Q4_K_M | 1.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NinjaMouse-2.4B-32L-danube-GGUF/resolve/main/NinjaMouse-2.4B-32L-danube.Q5_K_S.gguf) | Q5_K_S | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/NinjaMouse-2.4B-32L-danube-GGUF/resolve/main/NinjaMouse-2.4B-32L-danube.Q5_K_M.gguf) | Q5_K_M | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/NinjaMouse-2.4B-32L-danube-GGUF/resolve/main/NinjaMouse-2.4B-32L-danube.Q6_K.gguf) | Q6_K | 2.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NinjaMouse-2.4B-32L-danube-GGUF/resolve/main/NinjaMouse-2.4B-32L-danube.Q8_0.gguf) | Q8_0 | 2.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/NinjaMouse-2.4B-32L-danube-GGUF/resolve/main/NinjaMouse-2.4B-32L-danube.f16.gguf) | f16 | 4.9 | 16 bpw, overkill |
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](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.
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