
![](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 }
}
```
{"license": "cc-by-nc-4.0", "tags": ["mlabonne/NeuralMarcoro14-7B", "dpo", "7B", "winograd", "mmlu_abstract_algebra", "mistral"], "datasets": ["hromi/winograd_dpo_basic"], "base_model": "mlabonne/NeuralMarcoro14-7B"}
transformers safetensors mistral text-generation mlabonne/NeuralMarcoro14-7B dpo 7B winograd mmlu_abstract_algebra dataset:hromi/winograd_dpo_basic base_model:mlabonne/NeuralMarcoro14-7B doi:10.57967/hf/1611 license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us