# Sovereign-Swarm-Coherence-v1 by Alfredo Medina: Open Model Source: https://savrn.com/models/sovereign-swarm-coherence-v1 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve Sovereign-Swarm-Coherence-v1 (1M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 7, 2026. [Sovereign-Swarm-Coherence-v1 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/sovereign-swarm-coherence-v1/gpus) ## Model Card By Alfredo Medina, published under apache-2.0, revision 461b1355d60a. ### Sovereign Swarm Coherence Transformer v1 Published by ItsNotAI LABS (Dallas, Texas) The Sovereign-Swarm-Coherence-v1 is a production-verified PyTorch Multi-Head Self-Attention Transformer model designed for Robotics & Micro-Drone Swarm Control. ### Mathematical Physics & Explicit Parameter Breakdown Unlike generic models with arbitrary weight reporting, this repository explicitly itemizes learned trainable parameters versus non-trainable positional encoding constants: - Trainable Learned Parameters (requires_grad=True): 800,019 - Positional Encoding Constant Buffer Elements (pos_encoder.pe): 640,000 - Total Model State Tensor Elements: 1,440,019 - Checkpoint File Size: 5.51 MB - Trained Optimizer: AdamW (10 Epochs over domain datasets) #### Governing Mathematical Formulation $$R = \left| \frac{1}{N} \sum_{j=1}^{N} e^{i \theta_j} \right|$$ ### Primary Use Cases & Capabilities - Computes exact Kuramoto phase order parameter R and 2D Euclidean pairwise collision risk geometry for micro-drone swarms. - Domain Application: Real-time multi-agent autonomous swarm synchronization, 0.5m collision proximity alerts, and phase coupling strength adjustment. - Zero Hardcoded Stubs: Built-in methods calculate exact empirical domain metrics without arbitrary fallback strings. [Read the full model card (226 words)](https://savrn.com/models/sovereign-swarm-coherence-v1/card) ## Configuration Architecture SovereignSwarmTransformer Stored precision float32 Model type transformer ## Identity and Version Repository ItsnotAilabs/Sovereign-Swarm-Coherence-v1 Publisher Alfredo Medina Task Feature extraction Modality Text Library Not stated by the source Parameters 1M parameters Languages Not stated by the source Revision 461b1355d60abf1abcdad49962ef8014536647ad First published 2026-09-11 Last updated 2026-09-20 ## Files and Weights 11 files, 17.9 MB in total. The weights are 2 files totalling 11.5 MB in bin, safetensors. Weights2 files · 11.5 MB Configuration4 files · 514.9 KB Documentation1 file · 2.3 KB Other3 files · 5.8 MB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 5.8 MB | 59778292ce48 | | pytorch_model.bin | Weights | 5.8 MB | 6737bba7ecf4 | | agent_helper.py | Configuration | 5.5 KB | — | | config.json | Configuration | 280 B | — | | metrics.json | Configuration | 519 B | — | | weights_dump.json | Configuration | 508.6 KB | — | | README.md | Documentation | 2.3 KB | — | | __pycache__/agent_helper.cpython-311.pyc | Other | 11.4 KB | — | | domain_knowledge_base.sqlite | Other | 12.3 KB | — | | model.onnx.data | Other | 5.8 MB | 1bdf54c759a9 | | .gitattributes | Repository | 1.6 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 11.5 MB [Download from Alfredo Medina](https://huggingface.co/ItsnotAilabs/Sovereign-Swarm-Coherence-v1) Released by Alfredo Medina through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 11.5 MB | | 16-bit | 0.0 GB | | 8-bit | 0.0 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About Sovereign-Swarm-Coherence-v1 ### How much GPU memory does Sovereign-Swarm-Coherence-v1 need? About 0 GB at 16-bit and 0 GB at 4-bit: the weights (1M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run Sovereign-Swarm-Coherence-v1 on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use Sovereign-Swarm-Coherence-v1 commercially? Yes. Sovereign-Swarm-Coherence-v1 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ## Similar Models 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. 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