Update README.md

README.md

@@ -15,7 +15,7 @@ language:
 *phi2-pro* is a fine-tuned version of **[microsoft/phi-2](https://huggingface.co/microsoft/phi-2)** on **[argilla/dpo-mix-7k](https://huggingface.co/datasets/argilla/dpo-mix-7k)**
 preference dataset using *Odds Ratio Preference Optimization (ORPO)*. The model has been trained for 1 epoch.
 
-## LazyORPO
+## 💥 LazyORPO
 
 This model has been trained using **[LazyORPO](https://colab.research.google.com/drive/19ci5XIcJDxDVPY2xC1ftZ5z1kc2ah_rx?usp=sharing)**. A colab notebook that makes the training 
 process much easier. Based on [ORPO paper](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fhuggingface.co%2Fpapers%2F2403.07691)
@@ -23,7 +23,7 @@ process much easier. Based on [ORPO paper](https://colab.research.google.com/cor
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/2h3guPdFocisjFClFr0Kh.png)
 
-#### What is ORPO?
+#### 🎭 What is ORPO?
 
 Odds Ratio Preference Optimization (ORPO) proposes a new method to train LLMs by combining SFT and Alignment into a new objective (loss function), achieving state of the art results.
 Some highlights of this techniques are:
@@ -34,9 +34,9 @@ Some highlights of this techniques are:
 * 📊 Mistral ORPO achieves 12.20% on AlpacaEval2.0, 66.19% on IFEval, and 7.32 on MT-Bench out Hugging Face Zephyr Beta
 
 
-#### Usage
+#### 💻 Usage
 
-python
+```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
@@ -54,8 +54,8 @@ outputs = model.generate(**inputs, max_length=200)
 text = tokenizer.batch_decode(outputs)[0]
 print(text)
 
+```
 
-
-## Evaluation
+## 🏆 Evaluation
 
 ### COMING SOON