PremAI Autonomous Fine-tuning System: Technical Architecture Documentation

💯 Our Autonomous FineTuning System is now live, an advanced framework that minimizes manual oversight in SmallLanguageModel!

By leveraging multi agent orchestration, robust hierarchical taxonomies, and LLM-based “judges,” this architecture accelerates data augmentation, parallelises fine-tuning, and provides near-human evaluations at scale.

🔬 Multi-Agent Coordination

• Employs specialized agents to classify tasks, synthesize high-fidelity data, and run domain-specific validations.
• Streamlines text transformation and code-generation workflows with dynamic task assignments.

⚙️ Distributed Fine-Tuning

• Manages multiple training jobs simultaneously through dynamic resource allocation.
• Ensures efficient use of computational infrastructure, even under fault conditions.

🤖 LLM-Powered Evaluation

• Integrates LLM “judges” for granular, human-aligned assessments.
• Produces nuanced feedback across correctness, coherence, and task adherence without relying on manual scoring.

🔥 Active Learning for Continual Improvement

• Incorporates user-generated feedback in real time for iterative model refinement.
• Maintains a continuous improvement loop that adapts to evolving use cases and data distributions.

We look forward to seeing how this system transforms SLM deployment and evaluation across diverse industry contexts, and we invite you to explore the accompanying technical documentation to learn more about our approach to autonomous dataaugmentation, distributed orchestration, and large-scale modelevaluation.

Here you can find our official release: https://lnkd.in/dayPAJ26