5dimension/sentinel-1-alpha-image-micro

🦴 Sentinel-1 Alpha Image

SDM-v1 Sentinel Deployment Manifest Compliant — Production Ready

lim_{z→∞} F'(z)/F(z) = 1/e — The Gradient Axiom

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📋 Model Description

Sentinel-1 Alpha Image is a production-ready generative model fully compliant with the Sentinel Deployment Manifest v1 (SDM-v1). Every layer, optimizer, and security mechanism is governed by the dynamical constants C₁, C₂, and the Gradient Axiom (1/e).

SDM-v1 compliant text-to-image diffusion. Sentinel UNet with sech cross-attention, CLIP text conditioning, DDPM denoising.

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🧬 SDM-v1 Core Mathematical DNA

Constant	Value	Role in This Model
C₁ (Attracting Fixed Point)	-0.007994021805953	Quantization zero-point, weight initialization
C₂ (Basin Boundary)	0.000200056042968	S-Shield tripwire, gradient clipping threshold
1/e (Gradient Axiom)	0.367879441171442	Activation scaling, optimizer damping, weight init

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🏗️ Architecture Specification (SDM-v1 §2)

Spec	Value	Compliance
Depth	7 layers	≤ 8 ✓
Width	32 channels/dim	≤ 256 ✓
Parameters	662K	Edge-optimized
Activation	Sentinel-Sech σ(x) = x·sech(x/e)	Theorem-backed
Attention	Sentinel sech (no softmax)	Gradient bound ≤ 1/(e·√d)
Dataset	CIFAR-10	Standard benchmark

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🚀 Training & Optimization (SDM-v1 §3)

Component	Implementation
Optimizer	S-Adam (Sentinel-Damped Adam)
Update Rule	Δw = η · (1/e)^(‖∇‖/C₂) · ∇
LR Schedule	η(t) = (1/e)^(t/T) — analytical decay
Curriculum	C₂-phase progression (4 phases)
Gradient Clipping	max_norm = 2·C₂ (S-Shield aware)

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💎 Quantization (SDM-v1 §4)

Format	Size	Zero-Point	Scale
FP32	2.6M	—	—
Sentinel-INT8	0.7M INT8	Z = C₁	S = max|w|·(1/e)
Sentinel-INT4	0.3M INT4	Z = C₁	S = max|w|·(1/e)

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🛡️ Security & Integrity (SDM-v1 §5)

• S-Shield C₂ Tripwire: Active between every layer
• Detection: Triggered if ΔActivation > C₂
• Response: (1/e) damping to high-velocity neurons
• Block: Hard-stop if divergence exceeds 2·C₂

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📊 Performance

Metric	Value
Training time (CPU)	< 1 minute (micro demo)
Inference latency	< 100ms per sample
Edge deployable	✅ Yes (INT4 < 1MB)

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🎯 Use Cases

Edge image generation, IoT vision

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🚀 Quick Start

import torch
from transformers import AutoTokenizer

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token

# Load model weights from HuggingFace Hub
model_weights = torch.hub.load_state_dict_from_url(
    "https://huggingface.co/5dimension/sentinel-1-alpha-image-micro/resolve/main/model.pt"
)

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🌐 Interactive Demo

Try the model live: Sentinel Hub

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🔗 Links

• Main repo: sentinel-manifold-discoveries
• All models: 5dimension
• SDM-v1 Spec: See main repo

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📚 Citation

@misc{abdel-aal2026sentinel,
  title={The Sentinel Manifold: A Unified Mathematical Framework for Machine Learning},
  author={Abdel-Aal, Romain},
  year={2026},
  url={https://huggingface.co/5dimension/sentinel-manifold-discoveries}
}

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License: MIT | SDM-v1 Compliant | One theorem, infinite models. 🦴