Astraea
A quantum machine learning framework for NLP tasks with GPU and tensor core acceleration. Designed for research and production-scale natural language processing applications.
Key Features
Quantum-Enhanced Models
Leverages quantum computing principles to enhance model performance for complex NLP problems.
GPU Optimized
Full tensor core acceleration for training and inference on modern NVIDIA GPUs.
Auto-Scaling Pipelines
Cloud-deployable pipelines with automatic scaling from research to production workloads.
Technical Architecture
Core Components
- Quantum-Aware Neural Architecture
- GPU Tensor Core Optimizations
- Hybrid CPU-GPU Execution Backend
Requirements
Python 3.10+
CUDA 11.8+ (with compatible GPU)
PyTorch 2.2+
Qiskit 0.45+
Installation
pip install git+https://github.com/eleneb/astraea.git
How to Contribute
Explore the Code
Start by browsing our GitHub repository and documentation to understand the project's architecture and coding standards.
GitHub RepositoryCreate a Fork
Clone the project locally and start making your changes. Create feature branches for new contributions.
Fork ProjectSubmit a PR
We'll review and test your changes, then merge them if they meet our quality standards and project requirements.
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