train-model-knowledge-injection

What problem does it solve?

It streamlines end-to-end model training and knowledge injection by turning repositories, PDFs, and documentation into a deployable model package that passes objective quality and safety evaluations.

Core Features & Use Cases

• Source-to-artifact lineage: Tracks source → parsed text → chunks → dataset → model artifact to preserve auditability.
• Hybrid training strategy: Selects RAG, fine-tuning, or hybrid automatically based on freshness needs and behavioral control requirements.
• Strict evaluation and repair loop: Enforces correctness, hallucination control, faithfulness to sources, and latency/cost gates, then iteratively repairs only the failing pipeline stages.

Quick Start

Ask your agent to train a hybrid model using a target model plus a Git repository, attached PDFs, and Markdown docs, and to produce an evaluation report with pass/fail gates and a deployment runbook.