Transformers
English
reasoning
preference_learning
nca
Inference Endpoints
mradermacher's picture
auto-patch README.md
bce5dba verified
metadata
datasets:
  - openbmb/UltraInteract_sft
  - openbmb/UltraInteract_pair
  - openbmb/UltraFeedback
exported_from: openbmb/Eurux-8x22b-nca
language:
  - en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
  - reasoning
  - preference_learning
  - nca

About

static quants of https://huggingface.co/openbmb/Eurux-8x22b-nca

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
PART 1 PART 2 IQ3_S 61.6 beats Q3_K*
PART 1 PART 2 PART 3 PART 4 Q8_0 149.5 fast, best quality

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.png

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.