Model Fine-Tuning

To fine-tune a model on the 0G testnet, you must discover available providers, upload datasets, calculate pricing, transfer funds, create tasks, and monitor status transitions until the encrypted model is downloaded. This orchestrates the entire CLI workflow securely.

Dataset upload and model training on 0G Compute requires confirming provider readiness, submitting dataset-rooted tasks, funding accounts, and tracking status lifecycle transitions. The process ensures proper handshakes before downloading and decrypting the final encrypted models.

No, you cannot use the 0G Compute network for mainnet operations. The fine-tuning orchestration is strictly limited to the testnet environment, explicitly forbidding mainnet operations and prohibiting refunds throughout the task lifecycle.

Provider acknowledgement during distributed GPU fine-tuning requires waiting for Delivered and Finished status checks before decrypting results. This secure delivery workflow ensures providers confirm task completion and prevents initiating refunds during the training process.

To estimate costs for a fine-tuning job on a distributed GPU network, you calculate dataset sizes for pricing after uploading your data. This cost estimation occurs before transferring funds and creating the training task on the 0G testnet.

0G testnet model training forbids refunds to enforce the secure delivery workflow. The system ensures dataset uploads, fund transfers, and provider acknowledgements follow the prescribed status lifecycle, only decrypting results after Delivered and Finished statuses are reached.