base_model:
- Open-Orca/Mistral-7B-OpenOrca
- NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
- S-miguel/The-Trinity-Coder-7B
- chihoonlee10/T3Q-Mistral-Orca-Math-DPO
exported_from: jambroz/sixtyoneeighty-4x7B-v2
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- Open-Orca/Mistral-7B-OpenOrca
- NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
- S-miguel/The-Trinity-Coder-7B
- chihoonlee10/T3Q-Mistral-Orca-Math-DPO
About
static quants of https://huggingface.co/jambroz/sixtyoneeighty-4x7B-v2
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
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 | IQ3_S | 10.6 | beats Q3_K* |
| GGUF | IQ3_M | 10.7 | |
| GGUF | Q3_K_M | 11.7 | lower quality |
| GGUF | Q4_K_S | 13.8 | fast, recommended |
| 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
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.