Separate training from inference

Training is the learning phase. Inference is the using phase.

During training, software reads batches of data, asks the model to predict an answer, measures the error, and updates the weights. Training also keeps temporary state such as gradients, optimizer values, and the current position in the data.

A training system saves checkpoints along the way. A checkpoint lets the run resume or gives researchers two stages they can compare.

After training, the final weights can be copied into a smaller inference package. Inference does not update the weights for every prompt. It loads them and uses them to calculate an output.

The path looks like this:

training data -> training code -> checkpoints -> released weights
                                                |
                                                v
                                           inference runtime
                                                |
                                                v
                                             response

Downloading released weights does not give you the original dataset, optimizer state, or exact training environment. It gives you enough learned state to run the model, and often enough to fine-tune it further.

Keep this distinction in mind when someone says a model is open. We need to ask which part is open.