metadata
license: mit
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 and a modified version of the Time-LLM framework.
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)