base_model: mgoin/Nemotron-4-340B-Instruct-hf
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:
- vllm
About
weighted/imatrix quants of https://huggingface.co/mgoin/Nemotron-4-340B-Instruct-hf
static quants are available at https://huggingface.co/mradermacher/Nemotron-4-340B-Instruct-hf-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 |
|---|---|---|---|
| PART 1 PART 2 | i1-IQ1_S | 74.9 | for the desperate |
| PART 1 PART 2 | i1-IQ1_M | 81.5 | mostly desperate |
| PART 1 PART 2 | i1-IQ2_XXS | 92.4 | |
| PART 1 PART 2 PART 3 | i1-IQ2_XS | 102.2 | |
| PART 1 PART 2 PART 3 | i1-IQ2_S | 108.9 | |
| PART 1 PART 2 PART 3 | i1-IQ2_M | 117.6 | |
| PART 1 PART 2 PART 3 | i1-Q2_K | 131.6 | IQ3_XXS probably better |
| PART 1 PART 2 PART 3 PART 4 | i1-Q4_K_S | 195.2 | optimal size/speed/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.