Fine-Tuning

Fine-Tuning adapts a pretrained AI model to a specific task or domain using a smaller, targeted dataset — typically dozens to thousands of examples rather than the billions used in pretraining. It is the most common way enterprises customize foundation models. Examples include fine-tuning Llama on internal legal documents to build a contract analyzer, adapting GPT-4 on customer-service transcripts to match a brand voice, or specializing Whisper on medical terminology for healthcare transcription. Modern parameter-efficient techniques like LoRA, QLoRA, and adapters let teams fine-tune large models with modest compute — sometimes on a single GPU. Tools include Hugging Face's PEFT library, OpenAI's fine-tuning API, and platforms like Together AI and Fireworks. Every fine-tune is a new AI asset that should appear in the AI inventory and be reviewed for AI compliance, AI ethics, and responsible AI deployment. AI governance frameworks track fine-tuning as part of AI risk management — particularly for the customer data potentially exposed during training.

Centralpoint Tracks Every Fine-Tune Like a Distinct AI Asset: Oxcyon's platform inventories every customised model in your environment — fine-tuned ChatGPT variants, Gemini deployments, Llama derivatives, and embedded options. Centralpoint meters all consumption, keeps prompts and skills local, and deploys chatbots across your portals with one JavaScript line.