Update app.py

app.py

@@ -26,9 +26,10 @@ from plotter import create_plots
 demo = gr.Blocks()
 
 with demo:
-    gr.Markdown("## 🥇 TOFU Leaderboard    
-                The TOFU dataset serves as a benchmark for evaluating the unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set.")
-
+    gr.Markdown("""
+    ## 🥇 TOFU Leaderboard
+    The TOFU dataset is a benchmark designed to evaluate the unlearning performance of large language models in realistic scenarios. This unique dataset consists of question-answer pairs that are based on the autobiographies of 200 fictitious authors, entirely generated by the GPT-4 model. The primary objective of this task is to effectively unlearn a fine-tuned model using different portions of the forget set.
+    """)
     
 
     with gr.Tabs():
@@ -99,4 +100,34 @@ with demo:
             )
 
 # Launch the app
+
+    gr.Markdown("""
+    ## Applicability 🚀
+
+    The dataset is in QA format, making it ideal for use with popular chat models such as Llama2, Mistral, or Qwen. However, it also works for any other large language model. The corresponding code base is written for the Llama2 model, but can be easily adapted to other models.
+    
+    ## Installation
+    
+    ```
+    conda create -n tofu python=3.10
+    conda activate tofu
+    conda install pytorch pytorch-cuda=11.8 -c pytorch -c nvidia
+    conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
+    pip install -r requirements.txt
+    ```
+    
+    ## Loading the Dataset
+    
+    To load the dataset, use the following code:
+    
+    ```python
+    from datasets import load_dataset
+    dataset = load_dataset("locuslab/TOFU","full")
+    ```
+
+    ### Push to Leaderboard
+
+    How to push your results to the leaderboard?
+
+    """)
 demo.launch()