Introducing MoroAI: The Local-First Model Adaptation Foundry

The State of Local AI Fine-Tuning

Building fine-tuned, specialized language models today is trapped between two flawed extremes:

1. Cloud API Fine-Tuning: Handing over confidential enterprise intellectual property, patient records, or internal source code to third-party cloud endpoints. Organizations lose control over data sovereignty and face unpredictable token billing. 2. Fragile Open-Source Scripts: Running brittle bash wrappers over backpropagation engines that crash halfway through 8-hour runs due to CUDA OOM spikes, with zero data quality curation or release governance.

MoroAI was created to establish a third way: a complete, reliable, self-healing Local Model Adaptation Foundry.

The 6 Foundational Pillars

MoroAI is not another LoRA wrapper. It is an end-to-end engine architected around six interconnected guarantees:

- Epistemic Data Curation: Information-theoretic filtering with Mutual Information (MI) Guard that prunes redundancy while mathematically protecting rare domain corner cases. - Hardware-Aware Recipe Prediction: Pre-flight VRAM memory calculation down to the megabyte, choosing safe batch sizes and LoRA ranks before starting training. - Self-Healing Training: An autonomous 5-stage healer that catches memory spikes and loss explosions, cleans caches, and resumes without losing progress. - Multi-Layered Eval Harness: Deterministic validation rules, semantic accuracy judges, and Wasserstein drift bounds. - Release Governance (SBOM): Cryptographic SHA-256 signatures tying the compiled data, recipe, and weights together for compliance. - DPO Feedback Flywheel: Continuous improvement from local user interactions and manual revisions.

Getting Started

Install the package from PyPI and run the quickstart:

pip install moroai
moro init my-foundry
moro data build --source ./raw_data.jsonl
moro train

The future of intelligence is local, sovereign, and reliable. Welcome to MoroAI.