Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+# Project SecureAi Labs
+
+This project is designed for fine-tuning language models using the Unsloth library with LoRA adapters, and it provides utilities for training, testing, and formatting data for various models like Phi-3, Gemma, and Meta-Llama.
+
+## Table of Contents
+1. [Prerequisites](#prerequisites)
+2. [File Descriptions](#file-descriptions)
+   - [TRAINER.ipynb](#traineripynb)
+   - [TESTER.ipynb](#testeripynb)
+   - [dataFormat.ipynb](#dataformatipynb)
+3. [Usage](#usage)
+   - [Environment Setup](#environment-setup)
+   - [Training a Model](#training-a-model)
+   - [Testing the Model](#testing-the-model)
+   - [Formatting Data](#formatting-data)
+4. [Additional Resources](#additional-resources)
+
+---
+
+## Prerequisites
+
+Before running the project, ensure you have the following:
+- A [Hugging Face](https://huggingface.co) account and token.
+- Google Colab or a local environment with Python 3.x and CUDA support.
+- Installed packages like `unsloth`, `huggingface_hub`, `peft`, `trl`, and others (automatically installed in the notebooks).
+
+NOTE GPU Requirements:
+
+```python
+models = [
+    'Phi-3.5-mini-instruct-bnb-4bit', # |Min Training Gpu : T4, Min Testing GPU: T4, Max Model size : 14.748 GB|
+    'gemma-2-27b-it-bnb-4bit',  # |Min Training Gpu: A100, Min Testing GPU: L4, Max Model size: 39.564GB|
+    'Meta-Llama-3.1-8B-Instruct-bnb-4bit' # |Min Training Gpu: T4, Min Testing GPU: T4, Max Model size : 22.168GB|
+]
+```
+
+Refer to the [Unsloth Documentation](https://unsloth.ai/) for more details.
+
+## File Descriptions
+
+### 1. `TRAINER.ipynb`
+
+This notebook is responsible for training a language model with LoRA adapters using the Unsloth library. The core functionality includes:
+- Loading a pre-trained model from Hugging Face using `FastLanguageModel`.
+- Attaching LoRA adapters for efficient fine-tuning of large models.
+- Setting training configurations (e.g., learning rate, number of epochs, batch size) using the `SFTTrainer` from the `transformers` library.
+- Optionally, resuming training from the last checkpoint.
+- Uploading checkpoints and models to Hugging Face during or after training.
+
+#### How to Use:
+1. Open this notebook in Google Colab or a similar environment.
+2. Ensure you have set up your Hugging Face token (refer to the section below for setup).
+3. Customize the training parameters if needed.
+4. Run the notebook cells to train the model.
+
+### 2. `TESTER.ipynb`
+
+This notebook handles the evaluation of a fine-tuned model. It allows testing the model's accuracy and efficiency on a test dataset using pre-defined metrics like accuracy, precision, recall, and F1 score. It provides the following functionalities:
+- Loads the fine-tuned model with its LoRA adapters.
+- Defines a function to evaluate the model's predictions on a test dataset.
+- Outputs accuracy and other classification metrics.
+- Displays confusion matrices for better insight into model performance.
+
+#### How to Use:
+1. Load this notebook in your environment.
+2. Specify the test dataset and model details.
+3. Run the evaluation loop to get accuracy, predictions, and metrics visualizations.
+
+### 3. `dataFormat.ipynb`
+
+This notebook formats datasets into the correct structure for training and testing models. It provides functionality to map raw text data into a format suitable for language model training, particularly for multi-turn conversations:
+- Formats conversations into a chat-based template using Unsloth's `chat_templates`.
+- Maps data fields like "role", "content", and user/assistant conversations.
+- Prepares the dataset for tokenization and input to the model.
+
+#### How to Use:
+1. Open the notebook and specify the dataset you wish to format.
+2. Adjust any template settings based on the model you're using.
+3. Run the notebook to output the formatted dataset.
+
+---
+
+## Usage
+
+### Environment Setup
+
+1. **Install Unsloth**:
+   The following command is included in the notebooks to install Unsloth:
+   ```bash
+   !pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
+   ```
+
+2. **Install Additional Dependencies**:
+   These dependencies are also required:
+   ```bash
+   !pip install --no-deps xformers==0.0.27 trl peft accelerate bitsandbytes triton
+   ```
+
+3. **Hugging Face Token Setup**:
+   - Add your Hugging Face token as an environment variable in Google Colab or in your local environment.
+   - Use the Hugging Face token to download models and upload checkpoints:
+     ```python
+     from google.colab import userdata
+     from huggingface_hub import login
+     login(userdata.get('TOKEN'))
+     ```
+
+### Training a Model
+
+1. Open `TRAINER.ipynb`.
+2. Customize the model, template, and LoRA settings in the notebook.
+3. Set training configurations (e.g., epochs, learning rate).
+4. Run the notebook to start the training process.
+
+The model will automatically be saved at checkpoints and uploaded to Hugging Face.
+
+### Testing the Model
+
+1. Load `TESTER.ipynb` in your environment.
+2. Load the fine-tuned model with LoRA adapters.
+3. Specify a test dataset in the appropriate format.
+4. Run the evaluation function to get predictions, accuracy, and other metrics.
+
+### Formatting Data
+
+1. Use `dataFormat.ipynb` to format raw data into a training-friendly structure.
+2. Map the conversation fields using the `formatting_prompts_func`.
+3. Output the formatted data and use it in the training or testing notebooks.
+
+---
+
+## Additional Resources
+
+- Unsloth Documentation: [Unsloth.ai](https://unsloth.ai/)
+- Hugging Face Security Tokens: [Hugging Face Tokens](https://huggingface.co/docs/hub/en/security-tokens)
+- For issues, please refer to each library's official documentation or GitHub pages.
+
+---