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-license: mit
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+license: mit
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+# EEG Forecasting with Llama 3.1-8B and Time-LLM
+
+This repository contains the code and model for forecasting EEG signals by combining the quantized Llama 3.1-8B model from [Hugging Face](https://huggingface.co/akshathmangudi/llama3.1-8b-quantized) and a modified version of the [Time-LLM](https://github.com/KimMeen/Time-LLM) framework.
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+## Overview
+
+This project aims to leverage large language models (LLMs) for time-series forecasting, specifically focusing on EEG data. The integration of Llama 3.1-8B allows us to apply powerful sequence modeling capabilities to predict future EEG signal patterns with high accuracy and efficiency.
+
+### Key Features
+
+- **Quantized Llama 3.1-8B Model**: Utilizes a quantized version of Llama 3.1-8B to reduce computational requirements while maintaining performance.
+- **Modified Time-LLM Framework**: Adapted the Time-LLM framework for EEG signal forecasting, allowing for efficient processing of EEG time-series data.
+- **Scalable and Flexible**: The model can be easily adapted to other time-series forecasting tasks beyond EEG data.
+
+## Getting Started
+
+### Prerequisites
+
+Before you begin, ensure you have the following installed:
+
+- Python 3.8+
+- PyTorch
+- Transformers (Hugging Face)
+- Time-LLM dependencies (see the original [Time-LLM repository](https://github.com/KimMeen/Time-LLM))
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+