FineTuning

Fine-Tuning

⚠️ Sample. An AI-generated demo of LLM Wiki Newsroom — the "open source AI" topic is just the example corpus.

Overview

Fine-tuning adapts a pre-trained model to a specific domain or task by continuing training on additional data, modifying or adding to the model's internal weights and parameters. It is the primary capability an OpenWeights release enables: because the weights are published, a user can tailor the model with its own data and deploy a customized system rapidly, avoiding the significant cost of training from scratch. This makes open-weights models attractive to organizations that need a customized model but lack the resources or expertise to build one. Fine-tuning does not require access to the original TrainingData, which is why it remains possible even when a provider withholds its training corpus.

Connections

• OpenWeights — the release model that makes fine-tuning practical
• ModelLicensing — license terms govern whether fine-tuned derivatives may be shared
• TrainingData — fine-tuning adds data without access to the original corpus
• OpenSourceAI — fine-tuning is one of the four freedoms (modify)