DPOB-NMTOB-7B-GGUF / README.md
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metadata
base_model: paulml/DPOB-NMTOB-7B
language:
  - en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
  - merge
  - mergekit
  - lazymergekit
  - eren23/dpo-binarized-NeutrixOmnibe-7B
  - paulml/OmniBeagleSquaredMBX-v3-7B-v2

About

static quants of https://huggingface.co/paulml/DPOB-NMTOB-7B

weighted/imatrix quants are available at https://huggingface.co/mradermacher/DPOB-NMTOB-7B-i1-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 Q2_K 2.8
GGUF Q3_K_S 3.3
GGUF Q3_K_M 3.6 lower quality
GGUF Q3_K_L 3.9
GGUF IQ4_XS 4.0
GGUF Q4_K_S 4.2 fast, recommended
GGUF Q4_K_M 4.5 fast, recommended
GGUF Q5_K_S 5.1
GGUF Q5_K_M 5.2
GGUF Q6_K 6.0 very good quality
GGUF Q8_0 7.8 fast, best quality
GGUF f16 14.6 16 bpw, overkill

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

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.