Tuning & Learning

Tuning & Learning transforms general-purpose language models into domain specialists. Using LoRA (Low-Rank Adaptation) fine-tuning and knowledge distillation, we adapt 72B parameter models to understand your industry's terminology, reasoning patterns, and quality standards — without retraining from scratch.

Domain Specialisation

A 72B parameter model knows a lot about everything. But your business needs a model that knows a lot about your specific things. Tuning & Learning bridges that gap through parameter-efficient fine-tuning that preserves the model's general intelligence while adding deep domain expertise.

Tuning Methods

• LoRA Fine-Tuning: Low-rank adaptation that modifies <2% of model parameters while achieving domain-specific performance gains of 15-40%
• Knowledge Distillation: Train smaller, faster models (27B, 8B) that inherit the reasoning patterns of larger models on your specific use cases
• Reinforcement from Feedback: Incorporate human expert feedback to align model outputs with your quality standards

The Training Pipeline

1. Data Preparation: Curate training examples from your documents with our annotation tools
2. Baseline Evaluation: Measure the untuned model against your specific use cases
3. Fine-Tuning: Apply LoRA adapters on your dedicated GPU infrastructure
4. Evaluation: A/B test tuned vs. base model on held-out examples
5. Deployment: Deploy the tuned model to your CorpusAI instance