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
static quants of https://huggingface.co/nvidia/Llama-3_1-Nemotron-51B-Instruct
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3_1-Nemotron-51B-Instruct-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 | 19.5 | |
| GGUF | Q3_K_S | 22.8 | |
| GGUF | Q3_K_M | 25.3 | lower quality |
| GGUF | Q3_K_L | 27.4 | |
| GGUF | Q4_K_S | 29.6 | fast, recommended |
| GGUF | Q4_K_M | 31.1 | fast, recommended |
| GGUF | Q5_K_S | 35.7 | |
| GGUF | Q6_K | 42.4 | very good quality |
| PART 1 PART 2 | Q8_0 | 54.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
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.