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bagel-20b-v04-GGUF

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metadata
base_model: jondurbin/bagel-20b-v04
datasets:
  - ai2_arc
  - allenai/ultrafeedback_binarized_cleaned
  - argilla/distilabel-intel-orca-dpo-pairs
  - jondurbin/airoboros-3.2
  - codeparrot/apps
  - facebook/belebele
  - bluemoon-fandom-1-1-rp-cleaned
  - boolq
  - camel-ai/biology
  - camel-ai/chemistry
  - camel-ai/math
  - camel-ai/physics
  - jondurbin/contextual-dpo-v0.1
  - jondurbin/gutenberg-dpo-v0.1
  - jondurbin/py-dpo-v0.1
  - jondurbin/truthy-dpo-v0.1
  - LDJnr/Capybara
  - jondurbin/cinematika-v0.1
  - WizardLM/WizardLM_evol_instruct_70k
  - glaiveai/glaive-function-calling-v2
  - jondurbin/gutenberg-dpo-v0.1
  - grimulkan/LimaRP-augmented
  - lmsys/lmsys-chat-1m
  - ParisNeo/lollms_aware_dataset
  - TIGER-Lab/MathInstruct
  - Muennighoff/natural-instructions
  - openbookqa
  - kingbri/PIPPA-shareGPT
  - piqa
  - Vezora/Tested-22k-Python-Alpaca
  - ropes
  - cakiki/rosetta-code
  - Open-Orca/SlimOrca
  - b-mc2/sql-create-context
  - squad_v2
  - mattpscott/airoboros-summarization
  - migtissera/Synthia-v1.3
  - unalignment/toxic-dpo-v0.2
  - WhiteRabbitNeo/WRN-Chapter-1
  - WhiteRabbitNeo/WRN-Chapter-2
  - winogrande
language:
  - en
library_name: transformers
license: other
license_link: https://huggingface.co/internlm/internlm2-20b#open-source-license
license_name: internlm2-20b
quantized_by: mradermacher

About

static quants of https://huggingface.co/jondurbin/bagel-20b-v04

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

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 8.3
GGUF IQ3_XS 9.1
GGUF Q3_K_S 9.5
GGUF IQ3_S 9.6 beats Q3_K*
GGUF IQ3_M 9.9
GGUF Q3_K_M 10.5 lower quality
GGUF Q3_K_L 11.3
GGUF IQ4_XS 11.6
GGUF Q4_K_S 12.2 fast, recommended
GGUF Q4_K_M 12.8 fast, recommended
GGUF Q5_K_S 14.5
GGUF Q5_K_M 14.8
GGUF Q6_K 17.1 very good quality
GGUF Q8_0 21.7 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.