A checkpoint is saved data capturing the state of a model, typically including parameters or weights, at a point during or after training. Checkpoints allow training to resume, models to be versioned, or specific model states to be evaluated and deployed.
Data that captures the state of a model's parameters either during training or after training is completed. For example, during training, you can: 1. Stop training, perhaps intentionally or perhaps as the result of certain errors. 2. Capture the checkpoint. 3. Later, reload the checkpoint, possibly on different hardware. 4. Restart training.
Data that captures the state of a model's parameters either during training or after training is completed. For example, during training, you can: 1. Stop training, perhaps intentionally or perhaps as the result of certain errors. 2. Capture the checkpoint. 3. Later, reload the checkpoint, possibly on different hardware. 4. Restart training. Within Gemini, a checkpoint refers to a specific version of a Gemini model trained on a specific dataset.