miqu-1-103b-i1-GGUF / README.md
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metadata
base_model:
  - 152334H/miqu-1-70b-sf
language:
  - en
library_name: transformers
quantized_by: mradermacher
tags:
  - mergekit
  - merge

About

weighted/imatrix quants of https://huggingface.co/wolfram/miqu-1-103b

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 22.1
GGUF i1-IQ2_XXS 27.7
GGUF i1-IQ2_XS 30.8
GGUF i1-IQ2_S 32.3
GGUF i1-IQ2_M 35.1
GGUF i1-IQ3_XXS 40.0 fast, lower quality
GGUF i1-IQ3_XS 42.5
GGUF i1-IQ3_S 45.0 fast, beats Q3_K*
GGUF i1-IQ3_M 46.5

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