Fine-tuning is specialising a pre-trained AI model on your data. Instead of starting from scratch, you start from a model that already “speaks” and teach it your domain.
With techniques like LoRA and QLoRA, fine-tuning is efficient even on modest hardware.
A language model specialised in your domain: terminology, communication style, policies and best practices.
Model trained to classify your data: review sentiment, product categories, ticket priority, request intent.
Recognition of entities specific to your sector: product codes, regulations, technical terminology.
Collection, cleaning, labelling and splitting of training/validation/test data.
Choosing the base model and strategy: full fine-tuning, LoRA, QLoRA or adapter layers.
Training with early stopping, validation on business-relevant metrics.
Model in production with versioning, A/B testing and automatic retraining pipeline.
Tell us about your case. In 30 minutes we analyse your context and tell you what’s feasible.
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