speechlessai/speechless-mistral-7b-dare-0.85

• AWQ model(s) for GPU inference.
• GPTQ models for GPU inference, with multiple quantisation parameter options.
• 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference

Experiment for DARE(Drop and REscale), most of the delta parameters can be directly set to zeros without affecting the capabilities of SFT LMs and larger models can tolerate a higher proportion of discarded parameters.

Merged with below DARE models.

weight_mask_rate: 0.85 / use_weight_rescale: True / mask_stratery: random / scaling_coefficient: 1.0

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
Intel/neural-chat-7b-v3-1	61.59	66.21	83.64	62.37	59.65	78.14	19.56
migtissera/SynthIA-7B-v1.3	59.34	62.12	83.45	62.65	51.37	78.85	17.59
bhenrym14/mistral-7b-platypus-fp16	58.71	63.05	84.15	64.11	45.07	78.53	17.36
jondurbin/airoboros-m-7b-3.1.2	58.75	61.86	83.51	61.91	53.75	77.58	13.87
teknium/CollectiveCognition-v1.1-Mistral-7B	62.92	62.12	84.17	62.35	57.62	75.37	15.62
uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b	62.06	64.33	84.4	63.72	52.52	78.37	21.38
speechless-mistral-7b-dare-0.85 (Merge 6 DARE models)	64.69	63.57	84.82	64.29	50.66	79.24	45.56