mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO_f16-GGUF

About

static quants of https://huggingface.co/cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO_f16

weighted/imatrix quants are available at https://huggingface.co/mradermacher/TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO_f16-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	22.5
GGUF	IQ3_XS	25.1
GGUF	Q3_K_S	26.4
GGUF	IQ3_S	26.5	beats Q3_K*
GGUF	IQ3_M	27.2
GGUF	Q3_K_M	29.3	lower quality
GGUF	Q3_K_L	31.9
GGUF	IQ4_XS	32.9
GGUF	Q4_K_S	34.7	fast, recommended
GGUF	Q4_K_M	36.8	fast, recommended
GGUF	Q5_K_S	42.0
GGUF	Q5_K_M	43.2
GGUF	Q6_K	50.0	very good quality
PART 1 PART 2	Q8_0	64.7	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.