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metadata
base_model: jondurbin/bagel-dpo-8x7b-v0.2
datasets:
  - ai2_arc
  - jondurbin/airoboros-3.2
  - codeparrot/apps
  - facebook/belebele
  - boolq
  - jondurbin/cinematika-v0.1
  - drop
  - lmsys/lmsys-chat-1m
  - TIGER-Lab/MathInstruct
  - cais/mmlu
  - Muennighoff/natural-instructions
  - openbookqa
  - piqa
  - Vezora/Tested-22k-Python-Alpaca
  - cakiki/rosetta-code
  - Open-Orca/SlimOrca
  - spider
  - squad_v2
  - migtissera/Synthia-v1.3
  - datasets/winogrande
  - nvidia/HelpSteer
  - Intel/orca_dpo_pairs
  - unalignment/toxic-dpo-v0.1
  - jondurbin/truthy-dpo-v0.1
  - allenai/ultrafeedback_binarized_cleaned
  - Squish42/bluemoon-fandom-1-1-rp-cleaned
  - LDJnr/Capybara
  - JULIELab/EmoBank
  - kingbri/PIPPA-shareGPT
language:
  - en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher

About

static quants of https://huggingface.co/jondurbin/bagel-dpo-8x7b-v0.2

weighted/imatrix quants are available at https://huggingface.co/mradermacher/bagel-dpo-8x7b-v0.2-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 17.4
GGUF Q3_K_S 20.5
GGUF Q3_K_M 22.6 lower quality
GGUF Q3_K_L 24.3
GGUF IQ4_XS 25.5
GGUF Q4_K_S 26.8 fast, recommended
GGUF Q4_K_M 28.5 fast, recommended
GGUF Q5_K_S 32.3
GGUF Q5_K_M 33.3
GGUF Q6_K 38.5 very good quality
GGUF Q8_0 49.7 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

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.