YottaJoule Fine-Tune Kit — Custom Model Builder

YJ-FINETUNE-KIT-1.0

What YJ-FINETUNE-KIT Does

Takes your proprietary domain data, uses YottaJoule-Core as the base, runs it through MAGNA-TRAIN-1.0's 6-stage training pipeline, and outputs a custom named model you own, deploy, and sell.

This is model building made accessible. No ML engineering team required. No GPU provisioning. No infrastructure management. You bring the data and the domain expertise. The kit brings everything else.

Four-Stage Pipeline

Stage 1 — Data Intake & Validation

• Accepts structured JSON, CSV, PDF, DOCX, or plain text training sets
• Data quality scoring — flags low-quality, contradictory, or out-of-domain samples before training
• PII scan — identifies and redacts personal data before it enters the training pipeline
• Domain classification — confirms data aligns with selected YJ-CORE vertical (Legal, Health, or Build)
• Minimum viable dataset threshold — warns if dataset is below recommended volume for meaningful fine-tuning
• Data lineage log — immutable record of what data was used to train what model version

Stage 2 — YJ-CORE Base Selection

• Select base vertical: YJ-LEGAL-CORE, YJ-HEALTH-CORE, or YJ-BUILD-CORE
• Inherit all governance training from base — Blackstone, Hippocrates, UPIES survive fine-tuning
• Governance preservation test — automated pre-training check confirms protocol reasoning intact
• Custom vertical naming — your model gets a name, a version, and a marketplace identity

Stage 3 — Training Configuration (via MAGNA-TRAIN-1.0)

• Training objective selection: instruction-following, domain Q&A, classification, extraction, reasoning
• LoRA / full fine-tune selection based on dataset size and compute budget
• Epoch and learning rate configuration with recommended defaults by dataset size
• Evaluation set auto-split — 80/20 train/eval, holdout set for post-training validation
• Live training monitor — loss curves, evaluation metrics, governance drift detection in real time
• Governance drift alert — if fine-tuning degrades protocol reasoning, training halts and alerts

Stage 4 — Engine Registration & Deployment

• Custom model registered as a named engine in MAGNA-SELECT-1.0 engine registry
• Inference endpoint provisioned on YJ-INFERENCE-API infrastructure
• Model card generated — training data summary, performance benchmarks, governance validation results
• Marketplace listing created — sell inference access to other builders via Alt+Shift+X
• Revenue model: set your own per-call price, Alt+Shift+X takes 30%, you keep 70%
• Version management — update the model with new data, old version archived, builders on old version notified

What You Own

Every model built with YJ-FINETUNE-KIT is yours. Apache 2.0 base means you can distribute weights, host independently, or white-label for enterprise clients. The marketplace listing is optional — you can keep the model private or share it with the world.