openNemo-9B-GGUF/README.md

base_model, datasets, language, library_name, license, license_link, license_name, mradermacher, quantized_by, tags

base_model	datasets	language	library_name	license	license_link	license_name	mradermacher	quantized_by	tags
empero-ai/openNemo-9B	nvidia/Nemotron-Post-Training-Dataset-v1 nvidia/Nemotron-Post-Training-Dataset-v2 nvidia/Nemotron-Pretraining-Dataset-sample nvidia/Nemotron-CC-v2 nvidia/Nemotron-CC-Math-v1 nvidia/Nemotron-Pretraining-SFT-v1	en es fr de it ja	transformers	other	https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/	nvidia-open-model-license	readme_rev 1	mradermacher	nvidia pytorch

About

static quants of https://huggingface.co/empero-ai/openNemo-9B

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/openNemo-9B-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	5.1
GGUF	Q3_K_S	5.2
GGUF	IQ4_XS	5.5
GGUF	Q3_K_M	5.5	lower quality
GGUF	Q3_K_L	5.6
GGUF	Q4_K_S	6.3	fast, recommended
GGUF	Q4_K_M	6.6	fast, recommended
GGUF	Q5_K_S	6.9
GGUF	Q5_K_M	7.2
GGUF	Q6_K	9.2	very good quality
GGUF	Q8_0	9.6	fast, best quality
GGUF	f16	17.9	16 bpw, overkill

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.