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-# 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.
-
----