Fine-tuning

Fine-tuning changes a model's own weights using examples from a specific task or domain, rather than changing what the model is given at the moment it answers. That makes it different from prompting or retrieval, which shape a single call without touching the model itself. The result is a model that has, in effect, practiced the exact kind of task you need repeatedly.

A regulated enterprise that wants a model to consistently match its own approved tone, terminology or classification scheme often gets there faster with fine-tuning than with an ever-longer prompt, and without sending a stream of live examples to a third party's training process.

Fine-tuning a smaller open model on a firm's own approved response templates so it drafts client-facing replies that already match house style, rather than prompting for that style on every single call.

In Dynamiq, deploy and fine-tune open models, including Llama, Gemma, Mistral and Qwen, on the platform's own or your self-hosted Kubernetes infrastructure, then serve the resulting LoRA adapters dynamically over one shared base model, without a separate deployment for every adapter.