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metadata
base_model: MaziyarPanahi/calme-2.4-rys-78b
datasets:
  - MaziyarPanahi/truthy-dpo-v0.1-axolotl
  - Intel/orca_dpo_pairs
language:
  - en
library_name: transformers
license: mit
model_creator: MaziyarPanahi
model_name: calme-2.4-rys-78b
quantized_by: mradermacher
tags:
  - chat
  - qwen
  - qwen2
  - finetune
  - chatml

About

weighted/imatrix quants of https://huggingface.co/MaziyarPanahi/calme-2.4-rys-78b

static quants are available at https://huggingface.co/mradermacher/calme-2.4-rys-78b-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 24.4 for the desperate
GGUF i1-IQ1_M 25.5 mostly desperate
GGUF i1-IQ2_XXS 27.4
GGUF i1-IQ2_XS 29.1
GGUF i1-IQ2_S 30.0
GGUF i1-IQ2_M 31.5
GGUF i1-Q2_K 31.9 IQ3_XXS probably better
GGUF i1-IQ3_XXS 34.1 lower quality
GGUF i1-IQ3_XS 35.2
GGUF i1-Q3_K_S 36.9 IQ3_XS probably better
GGUF i1-IQ3_S 37.0 beats Q3_K*
GGUF i1-IQ3_M 38.0
GGUF i1-Q3_K_M 40.4 IQ3_S probably better
GGUF i1-Q3_K_L 42.4 IQ3_M probably better
GGUF i1-IQ4_XS 42.7
GGUF i1-Q4_0 44.4 fast, low quality
GGUF i1-Q4_K_S 47.0 optimal size/speed/quality
PART 1 PART 2 i1-Q4_K_M 50.8 fast, recommended
PART 1 PART 2 i1-Q5_K_S 55.2
PART 1 PART 2 i1-Q5_K_M 58.4
PART 1 PART 2 i1-Q6_K 69.1 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.