GLM4-9B-Neon-v2-GGUF/README.md

base_model, datasets, language, library_name, license, quantized_by

base_model	datasets	language	library_name	license	quantized_by
allura-org/GLM4-9B-Neon-v2	allura-org/Celeste-Filtered allura-org/neon-41k EVA-UNIT-01/Lilith-v0.2	en	transformers	mit	mradermacher

About

static quants of https://huggingface.co/allura-org/GLM4-9B-Neon-v2

weighted/imatrix quants are available at https://huggingface.co/mradermacher/GLM4-9B-Neon-v2-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	4.1
GGUF	Q3_K_S	4.7
GGUF	Q3_K_M	5.1	lower quality
GGUF	Q3_K_L	5.3
GGUF	IQ4_XS	5.4
GGUF	Q4_K_S	5.9	fast, recommended
GGUF	Q4_K_M	6.3	fast, recommended
GGUF	Q5_K_S	6.8
GGUF	Q5_K_M	7.2
GGUF	Q6_K	8.4	very good quality
GGUF	Q8_0	10.1	fast, best quality
GGUF	f16	18.9	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.