Text Generation
QuantumPeer
OpenPeerLLM
PyTorch
English
quantum-llm
quantum-computing
chern-simons
neural-networks
causal-lm
decentralized-learning
transformer
boinc
decent-torch
lonscript
Eval Results (legacy)
Instructions to use OpenPeerAI/QuantumPeer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- QuantumPeer
How to use OpenPeerAI/QuantumPeer with QuantumPeer:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- OpenPeerLLM
How to use OpenPeerAI/QuantumPeer with OpenPeerLLM:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| library_name: quantumpeer | |
| license: cc-by-nc-4.0 | |
| language: | |
| - en | |
| tags: | |
| - quantum-llm | |
| - quantum-computing | |
| - openpeerllm | |
| - chern-simons | |
| - neural-networks | |
| - pytorch | |
| - causal-lm | |
| - decentralized-learning | |
| - transformer | |
| - boinc | |
| - decent-torch | |
| - lonscript | |
| pipeline_tag: text-generation | |
| datasets: | |
| - OpenPeerAI/OpenPeerLLM | |
| model-index: | |
| - name: OpenPeerLLM | |
| results: | |
| - task: | |
| name: Language Modeling | |
| type: text-generation | |
| dataset: | |
| name: Custom Text Dataset | |
| type: text | |
| metrics: | |
| - name: Epoch | |
| type: number | |
| value: 2 | |
| - name: Model Size | |
| type: text | |
| value: "1.82 GB" | |
| - name: Run Time | |
| type: text | |
| value: "2.5 minutes on Intel UHD Graphics 630" | |
| - name: Loss | |
| type: cross-entropy | |
| value: 7.11 | |
| # 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 | |