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README.md

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 You can see the process with instructions for creating the model in the notebook: [Aligning_DPO_phi3.ipynb](https://github.com/peremartra/Large-Language-Model-Notebooks-Course/blob/main/P2-MHF/Aligning_DPO_phi3.ipynb)
 
-To create it, we started with the Phi-3-Mini-4K-Instruct model and applied DPO alignment using the distilabel-capybara-dpo-7k-binarized dataset.
+To create it, we started with the [Phi-3-Mini-4K-Instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) model and applied DPO alignment using the [distilabel-capybara-dpo-7k-binarized dataset](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized).
 
 Phi-3 is a state-of-the-art model with 3.8 billion parameters that has outperformed other models with 7 billion parameters. The DPO alignment process has produced good results, modifying the model's responses and making them more similar to those in the capybara dataset.