base_model: perlthoughts/Starling-LM-alpha-8x7B-MoE
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
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 | 17.6 | |
| GGUF | IQ3_XS | 19.5 | |
| GGUF | IQ3_S | 20.7 | beats Q3_K* |
| GGUF | Q3_K_S | 20.7 | |
| GGUF | IQ3_M | 21.7 | |
| GGUF | Q3_K_M | 22.8 | lower quality |
| GGUF | Q3_K_L | 24.4 | |
| GGUF | IQ4_XS | 25.6 | |
| GGUF | Q4_K_S | 27.0 | fast, recommended |
| GGUF | Q4_K_M | 28.7 | fast, recommended |
| GGUF | Q5_K_S | 32.5 | |
| GGUF | Q5_K_M | 33.5 | |
| GGUF | Q6_K | 38.6 | very good 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
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.