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metadata
base_model: cloudyu/Llama-3-70Bx2-MOE
language:
  - en
library_name: transformers
license: llama3
quantized_by: mradermacher

About

weighted/imatrix quants of https://huggingface.co/cloudyu/Llama-3-70Bx2-MOE

static quants are available at https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-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 26.8 for the desperate
GGUF i1-IQ1_M 29.4 mostly desperate
GGUF i1-IQ2_XXS 33.9
GGUF i1-IQ2_XS 37.6
GGUF i1-IQ2_S 39.0
GGUF i1-IQ2_M 42.5
GGUF i1-Q2_K 46.9 IQ3_XXS probably better
GGUF i1-IQ3_XXS 49.2 lower quality
PART 1 PART 2 i1-IQ3_XS 52.3
PART 1 PART 2 i1-IQ3_S 55.2 beats Q3_K*
PART 1 PART 2 i1-Q3_K_S 55.2 IQ3_XS probably better
PART 1 PART 2 i1-IQ3_M 56.6
PART 1 PART 2 i1-Q3_K_M 61.2 IQ3_S probably better
PART 1 PART 2 i1-Q3_K_L 66.3 IQ3_M probably better
PART 1 PART 2 i1-IQ4_XS 68.0
PART 1 PART 2 i1-Q4_0 72.1 fast, low quality
PART 1 PART 2 i1-Q4_K_S 72.5 optimal size/speed/quality
PART 1 PART 2 i1-Q4_K_M 76.8 fast, recommended
PART 1 PART 2 i1-Q5_K_S 87.5
PART 1 PART 2 i1-Q5_K_M 90.1
PART 1 PART 2 PART 3 i1-Q6_K 104.2 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.