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FashionGPT-70B-V1.1-i1-GGUF

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metadata
base_model: ICBU-NPU/FashionGPT-70B-V1.1
datasets:
  - Open-Orca/OpenOrca
  - openchat/openchat_sharegpt4_dataset
  - LDJnr/Puffin
  - ehartford/samantha-data
  - OpenAssistant/oasst1
  - jondurbin/airoboros-gpt4-1.4.1
language:
  - en
library_name: transformers
license: llama2
quantized_by: mradermacher

About

weighted/imatrix quants of https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.1

static quants are available at https://huggingface.co/mradermacher/FashionGPT-70B-V1.1-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 14.6 for the desperate
GGUF i1-IQ1_M 16.0 mostly desperate
GGUF i1-IQ2_XXS 18.4
GGUF i1-IQ2_XS 20.4
GGUF i1-IQ2_S 21.5
GGUF i1-IQ2_M 23.3
GGUF i1-Q2_K 25.6 IQ3_XXS probably better
GGUF i1-IQ3_XXS 26.7 lower quality
GGUF i1-IQ3_XS 28.4
GGUF i1-IQ3_S 30.0 beats Q3_K*
GGUF i1-Q3_K_S 30.0 IQ3_XS probably better
GGUF i1-IQ3_M 31.0
GGUF i1-Q3_K_M 33.4 IQ3_S probably better
GGUF i1-Q3_K_L 36.2 IQ3_M probably better
GGUF i1-IQ4_XS 36.9
GGUF i1-Q4_0 39.1 fast, low quality
GGUF i1-Q4_K_S 39.3 optimal size/speed/quality
GGUF i1-Q4_K_M 41.5 fast, recommended
GGUF i1-Q5_K_S 47.6
GGUF i1-Q5_K_M 48.9
PART 1 PART 2 i1-Q6_K 56.7 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.