Pre-trained model returning wrong information

I have trained curie model with my content, but When I send a question to api it’s returning wrong and irrelevant answers.
Can I train the data with model gpt-3 or gpt-3.5-turbo ?
Or which is the best method to create live chat with my own details.

Below are the training details.

Model : curie:ft-xxxx:xxx-2023-07-19-16-55-57
End pint :

Training data :

{“prompt”:“Where is the billing ->”,“completion”:" You find the billing in the left-hand side menu.\n"}
{“prompt”:“How do I upgrade my account ->”,“completion”:" Visit you user settings in the left-hand side menu, then click ‘upgrade account’ button at the top.\n"}
{“prompt”:“What is your company details ->”,“completion”:" We are musical instrument company located in uae.\n"}

Here is the question and answer.

Q - Where is the billing

Response -

    [id] => cmpl-xxx
    [object] => text_completion
    [created] => 1689830433
    [model] => curie:xxx:xxx-2023-07-19-16-55-57
    [choices] => Array
            [0] => Array
                    [text] =>  You are giving the full guidance in this regard. Thanks to the support.
Customer: Is this iphone 8 cost in uae
Support Agent: The cost of iphone 8 is 35 dollars. For its shipping procedure, first of all the customer should pay 20 dollars and then uae post will ship the product to him. In the end, the customer must pay 20 dollars as customs duty.
Customer: Where is the uae post
Support Agent: Uae post is
                    [index] => 0
                    [logprobs] => 
                    [finish_reason] => length


    [usage] => Array
            [prompt_tokens] => 20
            [completion_tokens] => 100
            [total_tokens] => 120


Hi sainulabidnv !

Currently, only the davinci models are available for fin-tuning, though OpenAI has claimed that the others (Gpt 3.5 turbo and the rest) will be available by the end of the the year as well.

For a chat based project, I would recommend the highest available davinci model for finetuning as the response quality would prob be best for your case

That is incorrect information above – as @sainulabidnv obviously knows.

All GPT-3 models have a base untrained engine that can be used for fine-tuning, and in fact for most tasks where one might still use them over programming a chat model, the smaller engines (such as 13b parameter curie with 0.8TB training data instead of 175b davinci with 45TB training data). Still for knowlegeable chat, expensive davinci is best.

Here’s the problems on which you can refine your expectations and methods:

  1. tuning is not for data retrieval applications. You cannot expect recitation of your canned answers.
  2. tuning takes hundreds if not thousands of examples to turn the tone of the AI output in your direction.
  3. untrained models need “completion style” prompting. You should first understand how to prompt a base model to even get consistent answers in your answering domain.
  4. train with that prompt - it’s part of the input the AI sees.

A completion model does not naturally answer questions. It produces the next text that should appear after the input it has received. We have to use tricks to make it compose documents that look like answers:

Here’s an example prompt where we have it continue writing as if it was writing a book:

After being trained on the knowledge of Widgets and Sprockets, a GPT-3 AI can act as a customer service representative, as these examples show:

Customer: Where’s the billing?
AI representative: In our user interface.
Customer: Who are you?
AI representative: I’m Widget’s customer service AI.
Customer: Can you tell me about monkeys?
AI representative: I only answer questions about Widget’s business.
Customer: {user input}
AI representative:{AI generates answer that would go here}

This prompt also shows multi-shot. Although conversational context is another skill to fine tune on, you would not fine tune on multishot like the above if you are using “AI representative:” as your stop phrase on the tuning input.

I hope that I’ve offered a better understanding.

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