metadata
datasets:
- berkeley-nest/Nectar
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- reward model
- RLHF
- RLAIF
- moe
About
static quants of https://huggingface.co/perlthoughts/Starling-LM-alpha-8x7B-MoE
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 | 17.6 | |
| GGUF | Q3_K_S | 20.7 | |
| GGUF | Q3_K_M | 22.8 | lower quality |
| GGUF | Q4_K_S | 27.0 | fast, medium quality |
| PART 1 PART 2 | Q8_0 | 49.8 | 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.