InternVL3-14B-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
OpenGVLab/InternVL3-14B	OpenGVLab/MMPR-v1.2	multilingual	transformers	apache-2.0	https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE	qwen	readme_rev 1	mradermacher	internvl custom_code

About

static quants of https://huggingface.co/OpenGVLab/InternVL3-14B

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

weighted/imatrix quants are available at https://huggingface.co/mradermacher/InternVL3-14B-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	mmproj-Q8_0	0.5	multi-modal supplement
GGUF	mmproj-f16	0.8	multi-modal supplement
GGUF	Q2_K	5.9
GGUF	Q3_K_S	6.8
GGUF	Q3_K_M	7.4	lower quality
GGUF	Q3_K_L	8.0
GGUF	IQ4_XS	8.3
GGUF	Q4_K_S	8.7	fast, recommended
GGUF	Q4_K_M	9.1	fast, recommended
GGUF	Q5_K_S	10.4
GGUF	Q5_K_M	10.6
GGUF	Q6_K	12.2	very good quality
GGUF	Q8_0	15.8	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.