Fine-tuning a fine-tuned model does not mean extanding the previous dataset (prompt and complexion examples)

After a few days of trying. Fine-tuning an existing fine-tuned model does not mean adding more example to the fine-tuned models. It starts another new model from scratch, the same as fine-tuning a base model.
I thought it would be possible to add new examples to the previous fine-tuned model’s data if you fine-tune it again and again. It should be like this to avoid paying for the whole data again after you add some new example to your file later.

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According to the documentation you can do this:

Continue fine-tuning from a fine-tuned model

](OpenAI API)

If you have already fine-tuned a model for your task and now have additional training data that you would like to incorporate, you can continue fine-tuning from the model. This creates a model that has learned from all of the training data without having to re-train from scratch.

To do this, pass in the fine-tuned model name when creating a new fine-tuning job (e.g. -m curie:ft-<org>-<date>). Other training parameters do not have to be changed, however if your new training data is much smaller than your previous training data, you may find it useful to reduce learning_rate_multiplier by a factor of 2 to 4."

Do you mean it’s an ammendment of the previous data instead of replacing it from scratch?

An ammendment of previous data.
My goal would be to get a feel how much adding more data improves the models output.

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