Update README.md

README.md

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 This model is a new DPO fine-tune of our new open dataset [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs), on the [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) model. You can find more information of the "distilabeled" dataset used at this repo [argilla/distilabeled-Hermes-2.5-Mistral-7B](https://huggingface.co/argilla/distilabeled-Hermes-2.5-Mistral-7B/blob/main/README.md#introduction), and visit [distilabel](https://github.com/argilla-io/distilabel).
 
 The difference between this model and [argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp)
-is that this model has been fine-tuned for a whole epoch instead, so it has seen the whole dataset.
+is that this model has been fine-tuned for a whole epoch instead instead of 200 steps, so it has seen the whole dataset.
+
+## Training details
+
+As we did with [Notus](https://argilla.io/blog/notus7b/), we wanted a reproducible recipe to test the impact of data quality. 
+
+And we're lucky to have so many amazing folks in the open community contributing reproducible, easy-to-use training scripts and recipes. This time, [Maxime Labonne](https://twitter.com/maximelabonne) had shared a [Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) to fine-tune OpenHermes with DPO and the original Intel's dataset, perfect! We just updated the base model to [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp), and applied the same dataset recipe we used for [argilla/distilabeled-Hermes-2.5-Mistral-7B](https://huggingface.co/argilla/distilabeled-Hermes-2.5-Mistral-7B/blob/main/README.md#introduction):
+
+```python
+from datasets import load_dataset
+
+# Instead of this:
+# dataset = load_dataset("Intel/orca_dpo_pairs", split="train")
+
+# we did this
+dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train")
+
+dataset = dataset.filter(
+    lambda r: 
+        r["status"] != "tie" and 
+        r["chosen_score"] >= 8 and 
+        not r["in_gsm8k_train"]
+)
+```
+
+## Benchmark results
+For benchmarking we used the famous "Nous" or "Teknium" benchmark. You can find below an overview, including our first experiment with a less ambitious dataset filtering (removing ties and `score>5`).
+
+For running the benchmark we used another awesome contribution from Maxime: [LLM AutoEval](https://github.com/mlabonne/llm-autoeval), check it out!
+
+|          Model          |AGIEval|GPT4ALL|TruthfulQA|Bigbench|Average|
+|-------------------------|------:|------:|---------:|-------:|------:|
+|[argilla/distilabeled-Marcoro14-7B-slerp-full](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp-full)|  45.17|  **76.59**|     64.68|   **48.15**|  **58.65**|
+|[argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp)|   **45.4**|  76.47|     **65.46**|   47.19|  58.63|
+|[Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp)       |  44.66|  76.24|     64.15|   45.64|  57.67|
+|[argilla/distilabeled-Hermes-2.5-Mistral-7B](https://huggingface.co/argilla/distilabeled-Hermes-2.5-Mistral-7B)   |   44.64 |   73.35 |      55.96 |    42.21 |   54.04 |
+
+### Training Hardware
+
+We used 1 x A100 80GB in runpod for less than 2 hours.
+
+## Acknowledgements
+
+We'd like to thank the amazing open community and in particular:
+
+* The Intel team for publishing a great open dataset and show how well it worked in the first place 
+* Teknium and NousResearch for their awesome work and models.
+* Maxime for sharing such great resources.