--- base_model: mlabonne/NeuralMarcoro14-7B license: apache-2.0 tags: - mlabonne/NeuralMarcoro14-7B - dpo - 7B - winograd - mmlu_abstract_algebra - mistral datasets: - hromi/winograd_dpo_basic --- ![](https://wizzion.com/solarpunk_turdus.webp) # udkai_Turdus A less contaminated version of [udkai/Garrulus](https://huggingface.co/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](https://huggingface.co/mlabonne/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. | Model | ARC | HellaSwag | MMLU | Truthful QA | GSM8K | Average | | -----------------------------|------ | --------- | ---- | ----------- | ------| ------- | | mlabonne/NeuralMarcoro14-7B | 71.42 | 87.59 | 64.84| 65.64 | 70.74 | 72.046 | | udkai/Turdus | 73.38 | 88.56 | 64.52| 67.11 | 67.7 | **72,254** | 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 } } ```