| ---
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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
|
| metrics:
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| - name: Epoch
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| type: number
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| value: 2
|
| - name: Model Size
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| type: text
|
| value: "1.82 GB"
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| - name: Run Time
|
| type: text
|
| value: "2.5 minutes on Intel UHD Graphics 630"
|
| - name: Loss
|
| type: cross-entropy
|
| value: 7.11
|
| ---
|
|
|
| # OpenPeerLLM: A Decentralized Large Language Model
|
|
|
| [](https://doi.org/10.57967/hf/6469)
|
|
|
| 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.
|
|
|
| ## 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
|
|
|
| - 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.
|
|
|
| ## Usage
|
|
|
| ```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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| ```
|
|
|
| ## 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.
|
|
|
| ### 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
|
|
|
| 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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|
|
| #### Example Prompts
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| 
|
|
|
| **Test Tokenizer:** https://www.kaggle.com/code/quantportal/test-tokenizer/
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|
|
| **Default Notebook:** https://www.kaggle.com/code/quantportal/openpeerllm-base-notebook
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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
|
|
|
| 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
|
|
|
| The system consists of several key components:
|
|
|
| 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
|
|
|
| This project is licensed under multiple licenses to ensure maximum flexibility and openness:
|
| - OPNL and OPNL-2 for the decentralized protocol aspects
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| - MIT License for the software implementation
|
| - Creative Commons Attribution 4.0 International (CC-BY-4.0) for documentation and models
|
|
|
| ## Citation
|
|
|
| ```bibtex
|
| @misc{openpeer-llm,
|
| author = {Andrew Magdy Kamal Nassief},
|
| title = {OpenPeerLLM: A Decentralized Language Model},
|
| year = {2025},
|
| publisher = {Stark Publishing Group},
|
| journal = {Hugging Face Model Hub}
|
| }
|
| ```
|
|
|
| ## Contributing
|
|
|
| Contributions are welcome! Please feel free to submit a Pull Request. |
