---
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
---
# Turdus-7B-GGUF


## Description

This repo contains GGUF format model files for Turdus-7B-GGUF.

## Files Provided

|          Name          |  Quant  | Bits | File Size |              Remark              |
| ---------------------- | ------- | ---- | --------- | -------------------------------- |
| turdus-7b.IQ3_XXS.gguf | IQ3_XXS |  3   |  3.02 GB  | 3.06 bpw quantization            |
| turdus-7b.IQ3_S.gguf   | IQ3_S   |  3   |  3.18 GB  | 3.44 bpw quantization            |
| turdus-7b.IQ3_M.gguf   | IQ3_M   |  3   |  3.28 GB  | 3.66 bpw quantization mix        |
| turdus-7b.Q4_0.gguf    | Q4_0    |  4   |  4.11 GB  | 3.56G, +0.2166 ppl               |
| turdus-7b.IQ4_NL.gguf  | IQ4_NL  |  4   |  4.16 GB  | 4.25 bpw non-linear quantization |
| turdus-7b.Q4_K_M.gguf  | Q4_K_M  |  4   |  4.37 GB  | 3.80G, +0.0532 ppl               |
| turdus-7b.Q5_K_M.gguf  | Q5_K_M  |  5   |  5.13 GB  | 4.45G, +0.0122 ppl               |
| turdus-7b.Q6_K.gguf    | Q6_K    |  6   |  5.94 GB  | 5.15G, +0.0008 ppl               |
| turdus-7b.Q8_0.gguf    | Q8_0    |  8   |  7.70 GB  | 6.70G, +0.0004 ppl               |

## Parameters

| path         | type    | architecture       | rope_theta | sliding_win | max_pos_embed |
| ------------ | ------- | ------------------ | ---------- | ----------- | ------------- |
| udkai/Turdus | mistral | MistralForCausalLM | 10000.0    | 4096        | 32768         |

## Benchmarks

![](https://i.ibb.co/jgS4ZNP/Turdus-7-B.png)

## Specific Purpose Notes

This model understands classification very well. Given the task to evaluate Indonesian clauses, it gives concise output in Indonesian:
![](https://i.ibb.co/bvtnyJ3/Evaluasi-Klausul-oleh-Turdus-7-B-Q8-0.png)

Even better in English (with slight different prompt):
![](https://i.ibb.co/1s1GLBn/Evaluasi-Klausul2-oleh-Turdus-7-B-Q8-0.png)

Excellent clause classification for evaluation preparation:
![](https://i.ibb.co/FwQYvRs/klasifikasi-pasal.png)

# Original Model Card

![](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 }
}
```