base_model: yuuko-eth/Rain-2x7B-MoE-32k-v0.1
language:
- zh
- en
library_name: transformers
license: unknown
model_name: Rain-2x7B-MoE-32k-v0.1
prompt_template: <s> SYS_PROMPT [INST] QUERY1 [/INST] RESPONSE1 [INST] QUERY2 [/INST]
quantized_by: mradermacher
tags:
- nlp
- chinese
- mistral
- mixtral
- traditional_chinese
- merge
- mergekit
- MediaTek-Research/Breeze-7B-Instruct-v0_1
- beowolx/CodeNinja-1.0-OpenChat-7B
- mlabonne/Marcoro14-7B-slerp
About
weighted/imatrix quants of https://huggingface.co/yuuko-eth/Rain-2x7B-MoE-32k-v0.1
static quants are available at https://huggingface.co/mradermacher/Rain-2x7B-MoE-32k-v0.1-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 | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| GGUF | i1-IQ3_M | 5.8 | |
| GGUF | i1-Q4_K_S | 7.4 | optimal size/speed/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. 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.