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metadata
base_model: nvidia/Llama-3_1-Nemotron-51B-Instruct
language:
  - en
library_name: transformers
license: other
license_link: >-
  https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
license_name: nvidia-open-model-license
quantized_by: mradermacher
tags:
  - nvidia
  - llama-3
  - pytorch

About

weighted/imatrix quants of https://huggingface.co/nvidia/Llama-3_1-Nemotron-51B-Instruct

static quants are available at https://huggingface.co/mradermacher/Llama-3_1-Nemotron-51B-Instruct-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 11.5 for the desperate
GGUF i1-IQ1_M 12.5 mostly desperate
GGUF i1-IQ2_XXS 14.1
GGUF i1-IQ2_XS 15.6
GGUF i1-IQ2_S 16.6
GGUF i1-IQ2_M 17.9
GGUF i1-Q2_K_S 18.0 very low quality
GGUF i1-Q2_K 19.5 IQ3_XXS probably better
GGUF i1-IQ3_XXS 20.3 lower quality
GGUF i1-IQ3_XS 21.7
GGUF i1-IQ3_S 22.8 beats Q3_K*
GGUF i1-Q3_K_S 22.8 IQ3_XS probably better
GGUF i1-IQ3_M 23.6
GGUF i1-Q3_K_M 25.3 IQ3_S probably better
GGUF i1-Q3_K_L 27.4 IQ3_M probably better
GGUF i1-IQ4_XS 27.8
GGUF i1-Q4_0 29.4 fast, low quality
GGUF i1-Q4_K_S 29.6 optimal size/speed/quality
GGUF i1-Q4_K_M 31.1 fast, recommended
GGUF i1-Q4_1 32.5
GGUF i1-Q5_K_S 35.7
GGUF i1-Q5_K_M 36.6
GGUF i1-Q6_K 42.4 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.