Some question about fine-tunning

API
1

I trained gpt-3.5-turbo with fine-tunning and got the model name and job id. I have a few questions regarding this situation:

1- Can I add new training data to the same model I trained?
2- Can I extract some training data to the model I trained?
3- What is the life expectancy of this model I trained? So will this model always be available? Is there a chance to somehow backup this model even if the endpoint is not removed?

2

Hi,

  1. Yes you can retrain already trained models, simply use the trained model name as the model to be trained.
  2. I do not know what you mean by extract, could you clarify please.
  3. The life expectancy is unknown, there have been no announcements regarding it, I would make the assumption that it will be for at least the life of the model it is based from.
1 Like
3

1- I use the following google colab to train the model. After model training is completed, it gives me a new model name. So when I entered “gpt-3.5-turbo” as the model to train, it gave me the new model “ft:gpt-3.5-turbo-0613:personal::abcdef”. If I enter the model ft:gpt-3.5-turbo-0613:personal::abcdef as the model to be trained, will it give a new model name as output? If it gives a different model name, it will naturally change at the endpoint. Isn’t there any way to prevent this?

2- I wanted to say, if I entered the wrong data into the model, can I remove this data?

4

  1. Not tried that, so you’d have to make a small test experiment to see if the old name gets updated or if you get a new name from the operation, either way you get the additional training.

  2. You cannot undo/delete training information.

5

I trained the gpt-3.5-turbo model with only 1 data. I noticed that the answer speed of this model I trained to any question was much faster than the normal gpt-3.5-turbo model. What is the reason of this? Is this because many people use the gpt-3.5-turbo endpoint and my model is only used by me?

6

A gpt-3.5-turbo fine tune does generate about 50% faster for me. You make reasonable guesses, but nobody at OpenAI is going to answer why models are faster or slower.

The tradeoff currently seen with fine-tune can be many seconds of latency, randomly, in beginning streamed output.