AI-Robotic-Labs/Self-Soverign-AI

Self Sovereign AI 1.0 with DeepSeek R1

This model card provides details for the "Self Sovereign AI 1.0 with DeepSeek R1" model, a hybrid AI integrating a Distributed Hash Table (DHT) for decentralized storage and a DeepSeek-inspired transformer for sequence processing.

Model Details

Model Description

• Developed by: [AI & Robotic Labs]
• Model type: Hybrid Neural Network (Feedforward + Transformer)
• Language(s) (NLP): Not language-specific; general-purpose architecture
• License: [MIT]

Model Sources [optional]

• Repository: https://huggingface.co/AI-Robotic-Labs/Self-Sovereign-AI

Uses

Direct Use

This model can be used for binary classification tasks (via the feedforward path) or sequence processing tasks (via the transformer path), such as time series analysis or tokenized data processing.

Downstream Use [optional]

The model can be fine-tuned for specific tasks like anomaly detection, sequence classification, or decentralized AI applications leveraging the DHT.

Out-of-Scope Use

Not suitable for large-scale language modeling or tasks requiring extensive pretraining due to its lightweight design.

Bias, Risks, and Limitations

• Bias: The model has not been trained on real-world data, so biases depend on the training dataset used by downstream users.
• Risks: The DHT implementation is a single-node simulation; it’s not production-ready for true decentralization.
• Limitations: The transformer is simplified (1 layer, small size), making it less powerful than full-scale models like BERT or GPT.

Recommendations

Users should evaluate the model on their specific datasets and consider scaling the transformer or DHT for production use. Be aware of the experimental nature of the DHT integration.

How to Get Started with the Model

Use the code below to load and use the model from Hugging Face:

from huggingface_hub import PyTorchModelHubMixin
import torch

# Load the model
model = SelfSovereignAI.from_pretrained("your-username/self-sovereign-ai-1.0-deepseek")

# Example: Feedforward inference
input_data_ff = torch.randn(1, 10)
output_ff = model(input_data_ff, use_transformer=False)
print(f"Feedforward output: {output_ff}")

# Example: Transformer inference
input_data_tr = torch.randn(1, 8, 10)  # batch_size, seq_length, input_size
output_tr = model(input_data_tr, use_transformer=True)
print(f"Transformer output: {output_tr}")

Training Details

Training Data

This model is untrained by default and serves as a base architecture. Users must provide their own training data.

Training Procedure

Preprocessing [optional]

For the transformer path, input data should be shaped as (batch_size, seq_length, input_size). For the feedforward path, use (batch_size, input_size).

Training Hyperparameters

• Training regime: User-defined (e.g., fp32, Adam optimizer recommended)

Speeds, Sizes, Times [optional]

• Model size: ~50 KB (untrained weights)
• Inference time: <1ms on CPU for small inputs (tested locally)

Evaluation

Testing Data, Factors & Metrics

Testing Data

Not evaluated; requires user-provided datasets.

Factors

Performance depends on task (classification vs. sequence processing) and dataset size.

Metrics

Recommended metrics: accuracy (classification), MSE (regression).

Results

No precomputed results available; performance varies by use case.

Summary

This is an experimental model combining decentralized storage (DHT) with a hybrid architecture (feedforward + transformer).

Model Examination [optional]

The DHT stores metadata and weights, accessible via model.get_metadata() and model.load_from_dht().

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator (Lacoste et al., 2019).

• Hardware Type: Local CPU (e.g., Intel i7)
• Hours used: <1 hour for development
• Cloud Provider: None
• Compute Region: Local
• Carbon Emitted: Negligible (<0.01 kg CO2e estimated)

Technical Specifications [optional]

Model Architecture and Objective

• Architecture: Feedforward (2 layers) + Transformer Encoder (1 layer, 2 heads)
• Objective: General-purpose binary classification or sequence processing

Compute Infrastructure

• Hardware: Developed on a standard CPU
• Software: PyTorch 2.x, huggingface_hub

Citation [optional]

BibTeX:

@misc{self_sovereign_ai_1.0_deepseek,
  author = {[AI & Robotic Labs]},
  title = {Self Sovereign AI 1.0 with DeepSeek R1},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/AI-Robotic-Labs/Self-Soverign-AI/}

APA:

AI & Robotic Labs (2025). Self Sovereign AI 1.0 with DeepSeek R1. Hugging Face. https://huggingface.co/AI-Robotic-Labs/Self-Soverign-AI/

Glossary [optional]

• DHT: Distributed Hash Table, a decentralized storage system.
• DeepSeek R1: Refers to the transformer component inspired by DeepSeek-style architectures.