Update README.md

README.md

@@ -7,15 +7,14 @@ license: apache-2.0
 Welcome to ***GEM_Testing_Arsenal***, where groundbreaking research meets practical power! This repository unveils a novel architecture for On-Device Language Models (ODLMs), straight from our paper, ["Fragile Mastery: are domain-specific trade-offs undermining On-Device Language Models?"](./link_to_be_insterted). With just a few lines of code, our custom `gem_trainer.py` script lets you train ODLMs that are more accurate than ever, tracking accuracy and loss as you go.
 
 ---
-
-### Highlights:
+## Highlights:
 - **Next-Level ODLMs**: Boosts accuracy with a new architecture from our research.
 - **Easy Training**: Call run_gem_pipeline to train on your dataset in minutes.
 - **Live Metrics**: Get accuracy and loss results as training unfolds.
 - **Flexible Design**: Works with any compatible dataset—plug and play!
 
 ---
-### Prerequisites:
+## Prerequisites:
 To dive in, you’ll need:
 - **Python** `3.8+`
 
@@ -24,11 +23,11 @@ To dive in, you’ll need:
 - **Git** *(to clone the repo)*
 
 ---
-### Quick Start:
+## Quick Start:
 
 1. **Clone the repository:**
     ```bash
-    git clone https://huggingface.co/GEM025/GEM_banking77
+    git clone https://huggingface.co/GEM025/GEM_Arsenal
     ```
 
 2. **Install Dependencies:**
@@ -52,13 +51,54 @@ Create a new python file and execute the code like:
     ```
 
 > ***Boom—your ODLM is training with boosted accuracy!***
+---
+## Running on Colab/Kaggle?
+
+Well it's pretty similar to the local run.
+
+```python
+""" This is very recommended to run for clean ouput during trains...
+
+import warnings 
+warnings.filterwarnings('ignore')
+
+"""
+
+#@ Step 1: Clone the github repo 
+!git clone https://huggingface.co/GEM025/GEM_Arsenal
+
+#@ Step 2: Install all requirements 
+!pip install -r /content/GEM/requirements.txt  #! For colab
+
+"""
+
+@! For kaggle:
+!pip install  -r /kaggle/working/GEM/requirements.txt
+
+"""
+
+#@ Step 3: Add repo to path
+import sys
+sys.path.append('/content/GEM')  #! Or /kaggle/working/GEM (for kaggle)
+
+#@ Step 4: Import and run function
+from gem_trainer import run_gem_pipeline
+from datasets import load_dataset
+
+#@ Rest of the code as above
+dataset = load_dataset("imdb")
+
+result = run_gem_pipeline(dataset, num_classes=2, num_epochs=2)
+
+print(result)
+```
 
 ---
-### Customizing Training:
+## Customizing Training:
 `run_gem_pipeline` keeps it simple, but you can tweak it! Dive into [`gem_trainer.py`](./gem_trainer.py) to adjust epochs, batch size, or other settings to fit your needs.
 
 ---
-### Contributing 💓 
+## Contributing 💓 
 Got ideas to make this even better? We’re all ears!
 - Fork the repo.
 - Branch off (`git checkout -b your-feature`).