Model Fine-Tuning

To fine-tune an AI model on a distributed GPU network, you upload a formatted training dataset, configure training parameters, and start a task. This tailors models to domain-specific data using 0G's testnet infrastructure.

Running a fine-tuning task on 0G requires a training dataset in the required format, a configuration file for training parameters, and a testnet wallet funded with 0G tokens to pay for the distributed GPU computation.

Yes, you can monitor training progress when fine-tuning machine-learning models on 0G. The workflow includes provider selection, dataset upload, task orchestration, and progress monitoring before retrieving your customized model.

Use distributed GPU fine-tuning for custom model adaptation when you need to improve accuracy on proprietary data. It is ideal for domain-specific retraining and rapid prototyping in testnet environments before final deployment.

Yes, 0G fine-tuning works with proprietary datasets for domain-specific retraining. A fintech team can train a risk model on internal data and deploy the customized version for evaluation in a sandbox.