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Model Tuning & Distillation

Adapt foundation models to your domain expertise

Model Tuning & Distillation

Model Tuning & Distillation is the implementation service behind the Tuning & Learning product. Our ML engineers work with your domain experts to prepare training data, run fine-tuning experiments, evaluate results, and deploy adapted models to your CaveauAI infrastructure.

Domain Model Specialisation

Fine-tuning a language model is not just a technical exercise — it is a collaboration between ML engineers and domain experts. The engineers know how to adjust model parameters efficiently. The domain experts know what a correct answer looks like. Model Tuning & Distillation brings both together in a structured process.

Service Phases

  1. Domain Analysis: Understand your terminology, reasoning patterns, and quality standards
  2. Data Curation: Prepare training examples with your team using our annotation tools
  3. Experimentation: Run multiple fine-tuning configurations and evaluate against baselines
  4. Distillation: Create smaller, faster models that retain domain-specific performance
  5. Deployment: Integrate tuned models into your CorpusAI instance
  6. Monitoring: Track model performance and schedule periodic retuning
Tuning Methodology
  • LoRA parameter-efficient fine-tuning
  • Progressive knowledge distillation
  • A/B evaluation with domain expert judges
  • Continuous performance monitoring
  • Periodic retuning schedule

Ready to Turn This Into a Live Programme?

We can scope the delivery model, identify the right team shape, and outline the fastest practical path forward.

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