The Elora Taurus Project

What Elora Does Differently

Elora uses a custom inference pipeline where each stage can be tuned, reordered, enabled, or disabled based on runtime and governance requirements.

Custom LLM Pipeline

Elora runs a custom Python-based inference pipeline instead of a fixed one-shot model call path. Pipeline stages can be composed to match governed runtime behavior.

Fabric GPU Connectivity

Inference can route through custom Fabric connectivity for remote WorkerHost/GPU paths with controlled fallback behavior when capacity or host state changes.

Runtime-Aware Inference Signals

Elora surfaces runtime-aware inference signals during proposal flow so operators can assess shifting risk posture before commit authorization.

Precise Stage Control

Add or remove stages as runtime intent evolves.
Tune behavior at each stage instead of global-only settings.
Run controlled experiments with live temperatures and model alterations in real time.
Trigger early drift detection during proposal to support pre-commit triage.
Keep plugin and engine integration aligned with governed runtime decisions.

Inference Changelog Live Record

Versioned update log for LLM inference behavior, Fabric route changes, and WordPress plugin/runtime compatibility.