README.md

---
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
model-index:
- name: Turdus
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 73.38
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=udkai/Turdus
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 88.56
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=udkai/Turdus
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 64.52
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=udkai/Turdus
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 67.11
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=udkai/Turdus
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 86.66
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=udkai/Turdus
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 67.7
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=udkai/Turdus
      name: Open LLM Leaderboard
---

![](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 }
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_udkai__Turdus)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |74.66|
|AI2 Reasoning Challenge (25-Shot)|73.38|
|HellaSwag (10-Shot)              |88.56|
|MMLU (5-Shot)                    |64.52|
|TruthfulQA (0-shot)              |67.11|
|Winogrande (5-shot)              |86.66|
|GSM8k (5-shot)                   |67.70|