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metadata
base_model: monsterbeasts/LishizhenGPT
datasets:
  - bigscience/xP3mt
language:
  - ak
  - ar
  - as
  - bm
  - bn
  - ca
  - code
  - en
  - es
  - eu
  - fon
  - fr
  - gu
  - hi
  - id
  - ig
  - ki
  - kn
  - lg
  - ln
  - ml
  - mr
  - ne
  - nso
  - ny
  - or
  - pa
  - pt
  - rn
  - rw
  - sn
  - st
  - sw
  - ta
  - te
  - tn
  - ts
  - tum
  - tw
  - ur
  - vi
  - wo
  - xh
  - yo
  - zh
  - zu
library_name: transformers
license: bigscience-bloom-rail-1.0
quantized_by: mradermacher

About

static quants of https://huggingface.co/monsterbeasts/LishizhenGPT

weighted/imatrix quants are available at https://huggingface.co/mradermacher/LishizhenGPT-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 3.5
GGUF Q3_K_S 4.0
GGUF Q3_K_M 4.5 lower quality
GGUF IQ4_XS 4.7
GGUF Q3_K_L 4.8
GGUF Q4_0_4_4 4.9 fast on arm, low quality
GGUF Q4_K_S 5.0 fast, recommended
GGUF Q4_K_M 5.4 fast, recommended
GGUF Q5_K_S 5.8
GGUF Q5_K_M 6.1
GGUF Q6_K 6.8 very good quality
GGUF Q8_0 8.7 fast, best quality
GGUF f16 16.3 16 bpw, overkill

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.