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metadata
base_model: jondurbin/airoboros-34b-3.2
datasets:
  - jondurbin/airoboros-3.2
  - bluemoon-fandom-1-1-rp-cleaned
  - boolq
  - jondurbin/gutenberg-dpo-v0.1
  - LDJnr/Capybara
  - jondurbin/cinematika-v0.1
  - glaiveai/glaive-function-calling-v2
  - grimulkan/LimaRP-augmented
  - piqa
  - Vezora/Tested-22k-Python-Alpaca
  - mattpscott/airoboros-summarization
  - unalignment/toxic-dpo-v0.2
language:
  - en
library_name: transformers
license: other
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
license_name: yi-license
quantized_by: mradermacher

About

weighted/imatrix quants of https://huggingface.co/jondurbin/airoboros-34b-3.2

static quants are available at https://huggingface.co/mradermacher/airoboros-34b-3.2-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-IQ2_M 11.9
GGUF i1-Q2_K 12.9 IQ3_XXS probably better
GGUF i1-IQ3_XXS 13.4 lower quality
GGUF i1-IQ3_M 15.7
GGUF i1-Q3_K_M 16.8 IQ3_S probably better
GGUF i1-Q4_K_S 19.7 optimal size/speed/quality
GGUF i1-Q4_K_M 20.8 fast, recommended

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. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.