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metadata
base_model: MaziyarPanahi/Qwen1.5-8x7b-v0.1
datasets:
  - Crystalcareai/MoD-150k
language:
  - en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE
license_name: tongyi-qianwen
quantized_by: mradermacher
tags:
  - axolotl
  - generated_from_trainer
  - moe
  - qwen
  - mixtral
  - text-generation-inference

About

weighted/imatrix quants of https://huggingface.co/MaziyarPanahi/Qwen1.5-8x7b-v0.1

static quants are available at https://huggingface.co/mradermacher/Qwen1.5-8x7b-v0.1-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 8.8 for the desperate
GGUF i1-IQ1_M 9.6 mostly desperate
GGUF i1-IQ2_XXS 10.9
GGUF i1-IQ2_XS 11.9
GGUF i1-IQ2_S 12.1
GGUF i1-IQ2_M 13.2
GGUF i1-Q2_K 15.0 IQ3_XXS probably better
GGUF i1-IQ3_XXS 15.7 lower quality
GGUF i1-IQ3_XS 16.7
GGUF i1-IQ3_S 17.5 beats Q3_K*
GGUF i1-Q3_K_S 17.5 IQ3_XS probably better
GGUF i1-IQ3_M 18.3
GGUF i1-Q3_K_M 19.1 IQ3_S probably better
GGUF i1-Q3_K_L 20.3 IQ3_M probably better
GGUF i1-IQ4_XS 21.1
GGUF i1-Q4_0 22.3 fast, low quality
GGUF i1-Q4_K_S 22.4 optimal size/speed/quality
GGUF i1-Q4_K_M 23.7 fast, recommended
GGUF i1-Q5_K_S 26.7
GGUF i1-Q5_K_M 27.5
GGUF i1-Q6_K 31.6 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. 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.