HSLM (Hybrid Symbolic Language Model) training infrastructure — Ternary neural networks, Beal conjecture, zeroth-order optimization, Railway deployment.

• 🔢 HSLM Model — ~1.24M ternary parameters, ~248KB compressed
• 🧠 Sacred Attention — φ-weighted mechanism for HSLM
• 📐 Autograd — reverse-mode automatic differentiation
• 🤖 Zeroth-Order — perturb-and-measure optimization (no backprop)
• 🚂 T-JEPA — jigsaw predictive coding self-supervision
• 🌐 Railway Deployment — cloud farm for distributed training
• 📊 Benchmarks — MNIST, CIFAR-10, neural network tests

# Clone with zig-golden-float submodule
git clone --recursive https://github.com/gHashTag/trinity-training.git
cd trinity-training
git submodule update --init --recursive

src/
├── hslm/          (70+ files)
│   ├── model.zig
│   ├── trainer.zig
│   ├── train.zig
│   ├── autograd.zig
│   ├── attention.zig
│   ├── sacred_attention.zig
│   └── ...
├── bench/          benchmarks
├── data_loaders/  MNIST, CIFAR-10
└── tri/             training orchestration
data/               (208MB)

Golden Ratio mathematics meets computational physics and AI.

Repository	Purpose	Status
trinity	🎯 Orchestrator, agents, API, MCP server	✅ Main
zig-golden-float	🔢 Numeric core: GF16, TF3, VSA, JIT
trinity-training	🧠 ML: HSLM, benchmarks, datasets	✅ Here
t27	📜 Ternary SSOT + Rust bootstrap	📜 Language
vibee-lang	🎵 VIBEE language spec (.tri/.vibee)	📜 Language
zig-hdc	🧩 Hyperdimensional: VSA, HRR	✅
zig-sacred-geometry	📐 Sacred φ-geometry, Beal	✅
zig-physics	⚛️ Quantum: QCD, gravity, dark matter	✅
zig-knowledge-graph	🕸️ KG server + CLI	✅
zig-agents	🤖 Agents: MCP, autonomous	✅
zig-crypto-mining	💰 BTC mining + DePIN	✅
trinity-fpga	🔌 FPGA: Verilog synthesis	🔄 WIP

Cloud Platforms:

• Railway — multi-account farm for distributed training
• Fly.io — multi-region swarm deployment

MIT © gHashTag