base_model: Crystalcareai/CrystalMistral-24B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- eren23/dpo-binarized-NeuralTrix-7B
- macadeliccc/WestLake-7B-v2-laser-truthy-dpo
- Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp
- cognitivecomputations/WestLake-7B-v2-laser
About
static quants of https://huggingface.co./Crystalcareai/CrystalMistral-24B
weighted/imatrix quants are available at https://huggingface.co./mradermacher/CrystalMistral-24B-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 | 8.9 | |
| GGUF | Q3_K_S | 10.5 | |
| GGUF | Q3_K_M | 11.7 | lower quality |
| GGUF | Q3_K_L | 12.6 | |
| GGUF | IQ4_XS | 13.1 | |
| GGUF | Q4_K_S | 13.8 | fast, recommended |
| GGUF | Q4_K_M | 14.7 | fast, recommended |
| GGUF | Q5_K_S | 16.7 | |
| GGUF | Q5_K_M | 17.2 | |
| GGUF | Q6_K | 19.9 | very good quality |
| GGUF | Q8_0 | 25.8 | fast, best quality |
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.