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

base_model	datasets	language	library_name	license	mradermacher	quantized_by	tags
justinj92/gpt-oss-nemo-20b	HuggingFaceH4/Multilingual-Thinking	multilingual en es ar fr de zh ja ko hi ru	transformers	apache-2.0	readme_rev 1	mradermacher	multilingual reasoning thinking fine-tuned lora conversational

About

static quants of https://huggingface.co/justinj92/gpt-oss-nemo-20b

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

weighted/imatrix quants are available at https://huggingface.co/mradermacher/gpt-oss-nemo-20b-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	Q3_K_S	12.2
GGUF	Q2_K	12.2
GGUF	IQ4_XS	12.3
GGUF	Q3_K_M	13.0	lower quality
GGUF	Q3_K_L	13.4
GGUF	Q4_K_S	14.8	fast, recommended
GGUF	Q4_K_M	15.9	fast, recommended
GGUF	Q5_K_S	16.0
GGUF	Q5_K_M	17.0
GGUF	Q6_K	22.3	very good quality
GGUF	Q8_0	22.4	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.