udkai_-_Turdus-gguf

Quantization made by Richard Erkhov.

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Turdus - GGUF

• Model creator: https://huggingface.co/udkai/
• Original model: https://huggingface.co/udkai/Turdus/

Original model description:

base_model: mlabonne/NeuralMarcoro14-7B license: cc-by-nc-4.0 tags:

• mlabonne/NeuralMarcoro14-7B
• dpo
• 7B
• winograd
• mmlu_abstract_algebra
• mistral datasets:
• hromi/winograd_dpo_basic

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udkai_Turdus

A less contaminated version of udkai/Garrulus and the second model to be discussed in the paper Subtle DPO-Contamination with modified Winogrande increases TruthfulQA, Hellaswag & ARC.

Contrary to Garrulus which was obtained after 2 epochs, this model was obtained after one single epoch of "direct preference optimization" of NeuralMarcoro14-7B with [https://huggingface.co/datasets/hromi/winograd_dpo ] .

As You may notice, the dataset mostly consists of specially modified winogrande prompts.

But before flagging this (or recommending this to be flagged), consider this:

Subtle DPO-Contamination with modified Winogrande causes the average accuracy of all 5-non Winogrande metrics (e.g. including also MMLU and GSM8K) to be 0.2% higher than the underlying model.

Yes, as strange as it may sound, one can indeed increase ARC from 71.42% to 73.38 % with one single epoch of cca 1200 repetitive winograd schematas...

BibTex

Should this model - or quasi-methodology which lead to it - be of certain pratical or theoretical interest for You, would be honored if You would refer to it in Your work:

@misc {udk_dot_ai_turdus,
	author       = { {UDK dot AI, Daniel Devatman Hromada} },
	title        = { Turdus (Revision 923c305) },
	year         = 2024,
	url          = { https://huggingface.co/udkai/Turdus },
	doi          = { 10.57967/hf/1611 },
	publisher    = { Hugging Face }
}