Text Generation
OpenPeerLLM
Safetensors
PyTorch
English
causal-lm
decentralized-learning
transformer
boinc
decent-torch
lonscript
Eval Results (legacy)
Instructions to use OpenPeerAI/OpenPeerLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenPeerLLM
How to use OpenPeerAI/OpenPeerLLM 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
| from src.model import DecentralizedLLM | |
| from src.grammar import LonScriptGrammar | |
| def main(): | |
| # Initialize the model | |
| model = DecentralizedLLM("gpt2") # Using GPT-2 as base model, can be changed | |
| grammar = LonScriptGrammar() | |
| # Example usage | |
| input_text = "Analyze the impact of renewable energy on climate change" | |
| context = "Current global climate trends and renewable energy adoption rates" | |
| # Get model response with deep reasoning | |
| response = model.reason(context, input_text) | |
| # Apply LonScript grammar for enhanced understanding | |
| enhanced_response = grammar.apply_grammar_rules(response) | |
| print("Enhanced Response:", enhanced_response) | |
| if __name__ == "__main__": | |
| main() |