# Alfredo Medina: Open-Weight Models and Datasets
Source: https://savrn.com/model-publishers/itsnotailabs
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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## Models

Model · Feature extraction

### [Sovereign-Swarm-Coherence-v1](https://savrn.com/models/sovereign-swarm-coherence-v1)

[Alfredo Medina](https://savrn.com/model-publishers/itsnotailabs)

The Sovereign-Swarm-Coherence-v1 is a production-verified PyTorch Multi-Head Self-Attention Transformer model designed for Robotics & Micro-Drone Swarm Control. Unlike generic models with arbitrary weight reporting, this repository explicitly itemizes learned trainable parameters versus non-trainable positional encoding constants: - Trainable Learned Parameters (requiresgrad=True): 800,019 - Positional Encoding Constant Buffer Elements (posencoder.pe): 640,000 $$R = \left| \frac{1}{N} \sum{j=1}^{N} e^{i \thetaj} \right|$$ - Computes exact Kuramoto phase order parameter R and 2D Euclidean pairwise collision risk geometry for micro-drone swarms. Apache 2.0 License © ItsNotAI LABS

Open weights apache-2.0 1M parameters

[View model](https://savrn.com/models/sovereign-swarm-coherence-v1)

Model · Feature extraction

### [Sovereign-Swarm-Coherence-v1-alpha](https://savrn.com/models/sovereign-swarm-coherence-v1-alpha)

[Alfredo Medina](https://savrn.com/model-publishers/itsnotailabs)

The Sovereign-Swarm-Coherence-v1-alpha repository contains the lightweight ONNX & WebAssembly (WASM) client-side package for running inference directly inside web browsers and mobile apps with 0ms server latency and zero cloud API infrastructure costs. - 100% Offline Capable: Zero network dependencies after initial WASM module download. Apache 2.0 License © ItsNotAI LABS

Open weights apache-2.0 1M parameters

[View model](https://savrn.com/models/sovereign-swarm-coherence-v1-alpha)

## Explore More

- [All model publishers](https://savrn.com/model-publishers)
- [The model directory](https://savrn.com/models)
- [The dataset directory](https://savrn.com/datasets)

## Source

- Listed from their public repositories, read 2026-09-20.
- [Hugging Face profile](https://huggingface.co/ItsnotAilabs)
- [How the hub is built](https://savrn.com/model-hub/methodology)