mradermacher/llama-embed-nemotron-8b-GGUF

---

base_model: nvidia/llama-embed-nemotron-8b datasets:

• nvidia/embed-nemotron-dataset-v1

language:

• multilingual

library_name: transformers license: other license_link: LICENSE license_name: customized-nscl-v1 mradermacher: readme_rev: 1 quantized_by: mradermacher tags:

• transformers
• text
• sentence-similarity
• feature-extraction
• mteb
• mmteb

---

About

static quants of https://huggingface.co/nvidia/llama-embed-nemotron-8b

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

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

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	3.1
GGUF	Q3K_S	3.5
GGUF	Q3K_M	3.9	lower quality
GGUF	Q3K_L	4.2
GGUF	Q4K_S	4.5	fast, recommended
GGUF	Q4K_M	4.7	fast, recommended
GGUF	Q6_K	6.3	very good quality
GGUF	Q8_0	8.1	fast, best quality
GGUF	f16	15.1	16 bpw, overkill

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.