Vicuna-7B-Email-DPO-Hybrid-GGUF/README.md

base_model, language, library_name, model_name, mradermacher, quantized_by, tags

base_model	language	library_name	model_name	mradermacher	quantized_by	tags
pladee42/Vicuna-7B-Email-DPO-Hybrid	en	transformers	dpo_vicuna_20250721_045610	readme_rev 1	mradermacher	base_model:adapter:lmsys/vicuna-7b-v1.5 dpo lora transformers trl

About

static quants of https://huggingface.co/pladee42/Vicuna-7B-Email-DPO-Hybrid

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	2.6
GGUF	Q3_K_S	3.0
GGUF	Q3_K_M	3.4	lower quality
GGUF	Q3_K_L	3.7
GGUF	IQ4_XS	3.7
GGUF	Q4_K_S	4.0	fast, recommended
GGUF	Q4_K_M	4.2	fast, recommended
GGUF	Q5_K_S	4.8
GGUF	Q5_K_M	4.9
GGUF	Q6_K	5.6	very good quality
GGUF	Q8_0	7.3	fast, best quality
GGUF	f16	13.6	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.