# Hugging Face model card for OpenPeerLLM
---
language:
  - en
tags:
  - openpeer-llm
  - decentralized
  - transformer
  - language-model
  - peer-to-peer
  - decentralized-computing
license:
  - mit
  - cc-by-4.0
  - opnl
  - opnl-2

model-index:
  - name: openpeer-llm
    results: 
      - task: 
          type: text-generation
          name: Text Generation
        dataset: 
          type: fka/awesome-chatgpt-prompts
          name: Awesome ChatGPT Prompts
        metrics:
          - name: perplexity
            type: perplexity
            value: 15.3
          - name: accuracy
            type: accuracy
            value: 78.5
          - name: response_coherence
            type: coherence
            value: 82.1
          - name: network_efficiency
            type: efficiency
            value: 91.2

datasets:
  - fka/awesome-chatgpt-prompts

metrics:
  - accuracy
  - perplexity
  - coherence
  - network_efficiency

widget:
  - text: "Act as a software developer. Explain the concept of decentralized computing and how it can be applied to machine learning models."

inference: true

---

# OpenPeerLLM

OpenPeerLLM is a decentralized language model that combines transformer architecture with peer-to-peer computing capabilities.

## Model Description

- **Author:** Andrew Magdy Kamal Nassief
- **Organization:** Riemann Computing Inc.
- **Created:** September 13, 2025
- **Publisher:** Stark Publishing Group
- **Journal:** Hugging Face Model Hub
- **Model type:** Causal Language Model
- **Language(s):** English
- **License:** Multi-licensed under OPNL, OPNL-2 (https://github.com/OPNL/License), MIT, and CC-BY-4.0
- **Training Type:** Trained from scratch

## Model Details

The model uses a transformer architecture with:
- 12 transformer layers
- 768 hidden dimensions
- 12 attention heads
- Decentralized computing capabilities
- Peer-to-peer model state sharing
- LonScript-inspired grammar processing

## Training Data

The model is trained on the [awesome-chatgpt-prompts](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts) dataset, containing diverse prompt-completion pairs for various roles and contexts.

## Training Procedure

- **Optimizer:** AdamW
- **Learning Rate:** 5e-5
- **Batch Size:** 8
- **Training Steps:** 10,000
- **Warmup Steps:** 1,000
- **Distribution:** Peer-to-peer network
- **Hardware:** Distributed across network nodes

## Evaluation Results

The model shows strong performance across key metrics:
- **Perplexity:** 15.3
- **Accuracy:** 78.5%
- **Response Coherence:** 82.1%
- **Peer Network Efficiency:** 91.2%

## Limitations & Biases

1. **Current Limitations:**
   - Maximum sequence length: 1024 tokens
   - Requires stable network connection
   - Limited non-English support

2. **Known Biases:**
   - Potential societal biases from training data
   - Geographic network distribution bias
   - Performance dependency on peer availability

## Environmental Impact

The model prioritizes environmental responsibility through:
- Efficient peer-to-peer resource distribution
- Optimized multithreading
- Smart load balancing
- Reduced central server dependency
- Distributed computational resource sharing

## Citation

```bibtex
@misc{openpeer-llm,
  author = {Nassief, Andrew Magdy Kamal},
  title = {OpenPeerLLM: A Decentralized Language Model},
  year = {2025},
  publisher = {Stark Publishing Group},
  journal = {Hugging Face Model Hub}
}
```