Specialised and fine-tuned models
Models adapted for one job, such as coding or a small focused task, by fine-tuning, distillation or community modification of open weights.
Read first: Post-training: from predictor to assistant, Open vs closed weights.
D1 · StoryFor young children and total beginners
A general model is like a family doctor who knows a bit of everything.
Some models get extra training on one subject, like writing code. Then they become specialists.
Specialists can be smaller and cheaper. But they're weaker outside their subject.
Like: A family doctor versus a specialist.
D2 · PictureFor curious beginners
Fine-tuning continues training a model on examples from one area, such as code or medical notes. Methods like LoRA train only a small add-on, so it can be done on modest hardware.
Distillation trains a small student model to copy a big teacher model's answers, packing much of the skill into something cheap enough to run locally.
With open weights, anyone can do this. Community variants include coding models, role-play and language-specific tunes, and uncensored models, where refusals are trained out or removed directly from the weights (sometimes called abliteration).
Like: A family doctor versus a specialist.
Where it breaks: A specialist doctor still keeps their general training. Fine-tuning can quietly erode skills and safety behaviour the base model had, so a specialist model may be worse at things nobody tested.
D5 · FrontierFor experts
Open problems: fine-tuning can undo safety training cheaply, which is a core argument in the open-weights debate. Narrow fine-tunes can also cause surprising behaviour changes far outside the trained topic.
Specialists also age fast: a new general model can overtake last year's specialist, so the gap between them keeps moving.