base_model: trollek/NinjaMouse-3B-40L-danube
datasets:
- Weyaxi/sci-datasets
- LDJnr/Capybara
- vicgalle/alpaca-gpt4
- glaiveai/glaive-code-assistant
- garage-bAInd/Open-Platypus
- abacusai/SystemChat
- TIGER-Lab/MathInstruct
- jondurbin/airoboros-3.2
- teknium/GPTeacher-General-Instruct
- m-a-p/Code-Feedback
- m-a-p/CodeFeedback-Filtered-Instruction
- ajibawa-2023/Python-Code-23k-ShareGPT
- TinyPixel/claude_multiround_chat_1k
- derek-thomas/ScienceQA
- WhiteRabbitNeo/WRN-Chapter-1
- WhiteRabbitNeo/WRN-Chapter-2
- migtissera/Synthia-v1.3
- camel-ai/physics
- camel-ai/chemistry
- camel-ai/math
- camel-ai/biology
- ajibawa-2023/Code-74k-ShareGPT
- causal-lm/auto_cot
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- code
- art
About
static quants of https://huggingface.co/trollek/NinjaMouse-3B-40L-danube
weighted/imatrix quants are available at https://huggingface.co/mradermacher/NinjaMouse-3B-40L-danube-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs 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 | Q2_K | 1.2 | |
| GGUF | Q3_K_S | 1.4 | |
| GGUF | Q3_K_M | 1.5 | lower quality |
| GGUF | Q3_K_L | 1.7 | |
| GGUF | IQ4_XS | 1.7 | |
| GGUF | Q4_K_S | 1.8 | fast, recommended |
| GGUF | Q4_K_M | 1.9 | fast, recommended |
| GGUF | Q5_K_S | 2.1 | |
| GGUF | Q5_K_M | 2.2 | |
| GGUF | Q6_K | 2.5 | very good quality |
| GGUF | Q8_0 | 3.2 | fast, best quality |
| GGUF | f16 | 6.0 | 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, 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.