Update README.md

README.md

@@ -7,13 +7,14 @@ datasets:
 
 # Phi-2-ORPO
 
-**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)**
+**Phi-2-ORPO** 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
 
 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)
+process much easier. Based on [ORPO paper](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fhuggingface.co%2Fpapers%2F2403.07691). This notebook has been created by
+**[Zain Ul Abideen](https://huggingface.co/abideen)**
 
 #### What is ORPO?
 
@@ -37,7 +38,7 @@ torch.set_default_device("cuda")
 model = AutoModelForCausalLM.from_pretrained("abideen/phi2-pro", torch_dtype="auto", trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained("abideen/phi2-pro", trust_remote_code=True)
 
-inputs = tokenizer('''def print_prime(n):
+inputs = tokenizer('''
    """
    Write a detailed analogy between mathematics and a lighthouse.
    """''', return_tensors="pt", return_attention_mask=False)