miiqu-f16-i1-GGUF / README.md
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metadata
base_model: Infinimol/miiqu-f16
language:
  - en
  - de
  - fr
  - es
  - it
library_name: transformers
license: other
quantized_by: mradermacher
tags:
  - merge

About

weighted/imatrix quants of https://huggingface.co/Infinimol/miiqu-f16

static quants are available at https://huggingface.co/mradermacher/miiqu-f16-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 i1-IQ1_S 19.4 for the desperate
GGUF i1-IQ2_XXS 24.4
GGUF i1-IQ2_XS 27.0
GGUF i1-IQ2_S 28.4
GGUF i1-IQ2_M 30.8
GGUF i1-Q2_K 33.6 IQ3_XXS probably better
GGUF i1-IQ3_XXS 35.1 lower quality
GGUF i1-IQ3_XS 37.3
GGUF i1-Q3_K_S 39.3 IQ3_XS probably better
GGUF i1-IQ3_S 39.5 beats Q3_K*
GGUF i1-IQ3_M 40.8
GGUF i1-Q3_K_M 43.9 IQ3_S probably better
GGUF i1-Q3_K_L 47.8 IQ3_M probably better
GGUF i1-IQ4_XS 48.6
PART 1 PART 2 i1-Q4_K_S 51.7 optimal size/speed/quality
PART 1 PART 2 i1-Q4_K_M 54.6 fast, recommended
PART 1 PART 2 i1-Q5_K_S 62.6
PART 1 PART 2 i1-Q5_K_M 64.3
PART 1 PART 2 i1-Q6_K 74.5 practically like static Q6_K

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.