Update README.md

README.md

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 ---
 license: cc-by-nc-4.0
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+# <nama_model_direktori>
+
+
+## Description
+
+This repo contains GGUF format model files for <model_name_dir>.
+
+## Model Info
+
+| path | type | architecture | rope_theta | sliding_window | max_position_embeddings |
+| ---- | ---- | ------------ | ---------- | -------------- | ----------------------- |
+| udkai/Turdus | mistral | MistralForCausalLM | 10000.0 | 4096 | 32768 |
+
+## Provided Files
+
+| Name | Quant Method | Bits | File Size | Remark |
+| ---- | ------------ | ---- | --------- | -------- |
+| turdus-7b.IQ3_S.gguf | IQ3_S | 3 | 3.44 | 3.18 GB | 3.44 bpw quantization |
+| turdus-7b.IQ3_M.gguf | Q3_M | 3 | 3.66 | 3.28 GB | 3.66 bpw quantization mix |
+| turdus-7b.Q4_0.gguf | Q4_0 | 4 | 4.11 GB | 3.56G, +0.2166 ppl @ LLaMA-v1-7B |
+| 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 @ LLaMA-v1-7B |
+| turdus-7b.Q5_K_M.gguf | Q5_K_M | 5 | 5.13 GB | 4.45G, +0.0122 ppl @ LLaMA-v1-7B |
+| turdus-7b.Q6_K.gguf | Q6_K | 6 | 5.94 GB | 5.15G, +0.0008 ppl @ LLaMA-v1-7B |
+| turdus-7b.Q8_0.gguf | Q8_0 | 7 | 7.7 GB | 6.70G, +0.0004 ppl @ LLaMA-v1-7B |
+
+# Original Model Card
+
+---
+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
+---
+
+![](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 }
+}
+```