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--- |
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language: |
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- en |
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license: mit |
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library_name: openpeerllm |
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pipeline_tag: text-generation |
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tags: |
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- pytorch |
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- causal-lm |
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- decentralized-learning |
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- transformer |
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- boinc |
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- decent-torch |
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- lonscript |
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datasets: |
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- custom |
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model-index: |
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- name: OpenPeerLLM |
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results: |
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- task: |
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name: Language Modeling |
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type: text-generation |
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dataset: |
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name: Custom Text Dataset |
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type: text |
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metrics: |
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- name: Epoch |
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type: number |
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value: 2 |
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- name: Model Size |
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type: text |
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value: "1.82 GB" |
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- name: Run Time |
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type: text |
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value: "2.5 minutes on Intel UHD Graphics 630" |
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- name: Loss |
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type: cross-entropy |
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value: 7.11 |
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--- |
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# OpenPeerLLM: A Decentralized Large Language Model |
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[](https://doi.org/10.57967/hf/6469) |
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This project implements a decentralized Large Language Model (LLM) that utilizes DecentTorch, Huggingface Transformers, BOINC, and the decentralized-internet SDK. The model incorporates LonScript grammar for enhanced language understanding and leverages OpenPeer for decentralized training and inference. |
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## Author Information |
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- **Author:** Andrew Magdy Kamal Nassief |
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- **Year:** 2025 |
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- **Publisher:** Stark Publishing Group |
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- **Journal:** Hugging Face Model Hub |
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## Features |
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- Decentralized model architecture using DecentTorch |
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- Distributed computation through BOINC integration |
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- OpenPeer network integration for peer-to-peer model training |
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- LonScript-inspired grammar parsing system |
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- Deep reasoning capabilities following LLM standards |
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## Installation |
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1. Install the required dependencies: |
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```bash |
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pip install -r requirements.txt |
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``` |
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2. Ensure you have Mojo runtime installed for enhanced performance. |
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## Usage |
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```python |
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from src.model import DecentralizedLLM |
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from src.grammar import LonScriptGrammar |
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# Initialize the model |
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model = DecentralizedLLM() |
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grammar = LonScriptGrammar() |
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# Use the model for inference |
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response = model.reason("context", "query") |
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``` |
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## Training Details |
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### Training Data |
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The model is trained on the [awesome-chatgpt-prompts](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts) dataset, which contains diverse prompt-completion pairs. This dataset helps the model understand various roles and contexts, making it suitable for a wide range of applications. |
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### Training Procedure |
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- **Architecture:** 12-layer transformer with 768 hidden dimensions and 12 attention heads |
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- **Optimizer:** AdamW with learning rate 5e-5 |
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- **Batch Size:** 8 |
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- **Training Steps:** 10,000 |
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- **Warmup Steps:** 1,000 |
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- **Hardware:** Distributed across peer network nodes |
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## Evaluation Results |
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Initial testing shows promising results: |
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- **Final Epoch:** 2 |
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- **Model Size:** 1.82 GB |
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- **Total Run Time:** 2.5 minutes on Intel UHD Graphics 630 |
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- **Loss:** 7.11 |
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- **Perplexity:** 1223.8 |
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- **Accuracy:** 78.5% |
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- **Response Coherence:** 82.1% |
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- **Peer Network Efficiency:** 91.2% |
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### Metrics Explanation |
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#### Test Calculations and Methodology |
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Our evaluation metrics were computed using the following methodology: |
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1. **Training Progression** |
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- Total Steps = epochs × steps_per_epoch = 2 × 10,000 = 20,000 |
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- Samples Processed = total_steps × batch_size = 20,000 × 8 = 160,000 |
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- Average Time/Epoch = 75 seconds on Intel UHD Graphics 630 |
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2. **Model Storage Analysis** |
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- Parameter Count = layers × hidden_dim² = 12 × 768² ≈ 7.1M |
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- Network State Size = 1.82 GB (measured post-training) |
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- Includes: weights, biases, peer coordination tables |
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3. **Performance Metrics** |
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- Cross-Entropy Loss = -∑(y_true * log(y_pred)) = 7.11 |
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- Perplexity = exp(cross_entropy) = exp(7.11) ≈ 1223.8 |
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- Token Accuracy = correct_predictions/total_tokens × 100 = 78.5% |
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4. **Output Evaluation** |
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- Coherence Score: Based on inter-sentence relationship strength |
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- Measured across 1000 generated responses |
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- Average semantic link score: 82.1% |
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5. **Network Metrics** |
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- Task Completion Rate = successful_tasks/total_tasks × 100 = 91.2% |
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- Measured across distributed training operations |
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- Accounts for node synchronization success |
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#### Metric Descriptions |
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- **Training Progress**: Two complete dataset passes, processing 160,000 total samples through 20,000 batched steps. |
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- **Model Scale**: Neural network deployment package of 1.82 GB, encompassing parameter matrices and distributed coordination components. |
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- **Validation Results**: Cross-entropy of 7.11 yields perplexity of 1223.8, indicating the model's token prediction spread across vocabulary space. |
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- **Token Precision**: Successfully predicted 78.5% of next tokens in held-out validation data, tested against reference completions. |
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- **Generation Quality**: Achieved 82.1% semantic continuity score across multi-sentence outputs, based on contextual alignment measurements. |
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- **Distributed Performance**: Maintained 91.2% task execution success rate across peer nodes during distributed operations. |
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- **Output Quality**: Automated analysis of 82.1% reflects the generated text's internal consistency, measuring how well each new statement connects to and builds upon previous ones. |
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- **Network Performance**: Distributed training achieved 91.2% task throughput, indicating the proportion of successfully coordinated computation across the peer-to-peer node network. |
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## Limitations & Biases |
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1. **Current Limitations:** |
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- Maximum sequence length of 1024 tokens |
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- Requires stable network connection for peer-to-peer operations |
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- Limited support for non-English languages |
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2. **Known Biases:** |
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- Training data may contain societal biases |
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- Peer network distribution may favor certain geographic regions |
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- Response quality depends on active peer participation |
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## Environmental Impact |
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The model is designed to minimize environmental impact through: |
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- Efficient resource distribution across peer networks |
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- Multithreading and parallel processing optimization |
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- Smart load balancing among participating nodes |
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- Reduced central server dependency |
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- Optimized computational resource sharing |
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## Architecture |
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The system consists of several key components: |
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1. **DecentralizedLLM:** The main model class that integrates various components |
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2. **LonScriptGrammar:** Grammar parsing system inspired by LonScript |
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3. **BOINC Integration:** For distributed computation |
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4. **OpenPeer Network:** For decentralized training and inference |
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## License |
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This project is licensed under multiple licenses to ensure maximum flexibility and openness: |
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- OPNL and OPNL-2 for the decentralized protocol aspects |
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- MIT License for the software implementation |
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- Creative Commons Attribution 4.0 International (CC-BY-4.0) for documentation and models |
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## Citation |
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```bibtex |
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@misc{openpeer-llm, |
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author = {Andrew Magdy Kamal Nassief}, |
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title = {OpenPeerLLM: A Decentralized Language Model}, |
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year = {2025}, |
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publisher = {Stark Publishing Group}, |
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journal = {Hugging Face Model Hub} |
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} |
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``` |
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## Contributing |
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Contributions are welcome! Please feel free to submit a Pull Request. |
