QuantumPeer: Quantum-Enhanced OpenPeerLLM
Model Description
QuantumPeer implements a novel approach to language model execution by combining OpenPeerLLM with quantum circuit simulation inspired by the Chern-Simons theory. This hybrid approach enables unique quantum-classical interactions in natural language processing.
Intended Uses
- Research in quantum-enhanced language models
- Development of hybrid quantum-classical AI systems
- Educational purposes in quantum computing
- Natural language processing with quantum inspiration
Training Procedure
The model utilizes:
- Base Model: OpenPeerLLM
- Quantum Circuit: Custom implementation with Chern-Simons topology
- Integration: Quantum state influence on attention mechanisms
Technical Specifications
- Framework: PyTorch + Custom Quantum Simulator
- Parameters: Based on OpenPeerLLM architecture
- Input Format: Text prompts
- Output Format: Generated text with quantum enhancement
- Model Architecture: Hybrid quantum-classical
Limitations & Biases
- Simulation-based quantum computing (not real quantum hardware)
- Performance dependent on classical computing resources
- Inherits any limitations from base OpenPeerLLM model
Out-of-Scope Uses
- Production-critical applications
- Safety-critical systems
- Applications requiring true quantum hardware
Additional Information
License: CC-BY-NC-4.0/CC-BY-NC-SA - All rights reserved
Creators:
- OpenPeerAI
- Andrew Magdy Kamal Nassief
- Riemann Computing
Evaluation results
- Epoch on Custom Text Datasetself-reported2.000
- Model Size on Custom Text Datasetself-reported1.82 GB
- Run Time on Custom Text Datasetself-reported2.5 minutes on Intel UHD Graphics 630
- Loss on Custom Text Datasetself-reported7.110