Full Fine-Tuning

Full fine-tuning is the training approach that updates all of a pretrained model's weights on task-specific data, in contrast to PEFT methods like LoRA, adapter layers, and prefix tuning that update only a small fraction. Full fine-tuning maximizes adaptation flexibility and can achieve the highest possible task performance, but at substantial cost in compute, memory, storage, and operational complexity. A full fine-tune of a 70B-parameter model requires multiple high-end GPUs for days or weeks of training, costing tens of thousands of dollars per run. Storage is also a major consideration — each fully-fine-tuned variant produces a complete model copy (gigabytes to terabytes), whereas LoRA adapters are megabytes. Full fine-tuning also carries higher risk of catastrophic forgetting — losing capabilities the base model had — and overfitting on small datasets. Modern production practice reserves full fine-tuning for cases where PEFT is empirically insufficient: very large domain shifts, specialized scientific or legal applications, and frontier-scale research. AI governance teams document full fine-tunes with the same lineage rigor as base models because they are essentially new models from a deployment perspective.

Full fine-tuned models with Centralpoint: Centralpoint routes generation to fully fine-tuned models from any source — frontier labs, in-house research, third-party domain models — in a model-agnostic stack. Tokens are metered per skill, prompts stay local, and fine-tuned-model chatbots deploy through one line of JavaScript with complete audit trails.


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