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---
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
<!-- provided-files -->
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) 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](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q2_K.gguf) | Q2_K | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q3_K_S.gguf) | Q3_K_S | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q3_K_M.gguf) | Q3_K_M | 22.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q4_K_S.gguf) | Q4_K_S | 27.0 | fast, medium quality |
| [PART 1](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Starling-LM-alpha-8x7B-MoE-GGUF/resolve/main/Starling-LM-alpha-8x7B-MoE.Q8_0.gguf.part2of2) | 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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
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