Update README.md

README.md

@@ -17,6 +17,8 @@ tags:
 😸<a href="https://github.com/prsdm/fine-tuning-llms/blob/main/Fine-tuning-phi-2-model.ipynb">GitHub</a> •📝<a href="https://medium.com/@prasadmahamulkar/fine-tuning-phi-2-a-step-by-step-guide-e672e7f1d009">Article</a> • Models & Datasets on: 🤗<a href="https://huggingface.co/prsdm">Hugging Face</a>
 </p>
 
+![diagram](https://github.com/user-attachments/assets/b84531b3-9935-4e2f-bd05-e0f88f95edb6)
+
 This repository provides a collection of Jupyter notebooks that demonstrate how to fine-tune large language models using various tools and techniques. 
 
 fine-tuning or instruction tuning is the process where the pre-trained model is further trained on the smaller dataset to adapt its knowledge for a specific task or domain. This process tweaks the model’s parameters to perform specific tasks. In fine-tuning, there are two methods:
@@ -35,7 +37,3 @@ In RLHF, the model interacts with users, generates responses, and receives feedb
 | [llama-2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) | This model has been fine-tuned on a dataset with human-generated prompts to answer questions related to general knowledge. (used SFT method) | [Dataset](https://huggingface.co/datasets/prsdm/finance-llama2-1k) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/prsdm/fine-tuning-llms/blob/main/Fine-tuning-llama-2-model.ipynb) | [llama-2-finance](https://huggingface.co/prsdm/llama-2-finance) |
  
 
-### Diagram:
-
-![diagram](https://github.com/user-attachments/assets/b84531b3-9935-4e2f-bd05-e0f88f95edb6)
-