Fine-Tune the OpenAI Model

In this example we are going to teach an OpenAI model, how to write MindsDB AI SQL queries

All OpenAI models belong to the group of Large Language Models (LLMs). By definition, these are pre-trained on large amounts of data. However, it is possible to fine-tune these models with a task-specific dataset for a defined use case.

OpenAI supports fine-tuning of some of its models listed here. And with MindsDB, you can easily fine-tune an OpenAI model making it more applicable to your specific use case.

Let’s create a model to answer questions about MindsDB’s custom SQL syntax.

First, create an OpenAI engine, passing your OpenAI API key:

CREATE ML_ENGINE openai_engine
FROM openai
USING
    openai_api_key = 'your-openai-api-key';

Then, create a model using this engine:

CREATE MODEL openai_davinci
PREDICT completion
USING
    engine = 'openai_engine',
    model_name = 'davinci-002',
    prompt_template = 'Return a valid SQL string for the following question about MindsDB in-database machine learning: {{prompt}}';

You can check model status with this command:

DESCRIBE openai_davinci;

Once the status is complete, we can query for predictions:

SELECT prompt, completion
FROM openai_davinci as m
WHERE prompt = 'What is the SQL syntax to join input data with predictions from a MindsDB machine learning model?'
USING max_tokens=400;

On execution, we get:

+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------+
| prompt                                                                                            | completion                                                                                           |
+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------+
| What is the SQL syntax to join input data with predictions from a MindsDB machine learning model? | The SQL syntax is: SELECT * FROM input_data INNER JOIN predictions ON input_data.id = predictions.id |
+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------+

If you followed one of the MindsDB tutorials before, you’ll see that the syntax provided by the model is not exactly as expected.

Now, we’ll fine-tune our model using a table that stores details about MindsDB’s custom SQL syntax.

Upload this data file to MindsDB and use it to finetune the model.

This is how you can fine-tune an OpenAI model:

FINETUNE openai_davinci
FROM files
    (SELECT prompt, completion FROM openai_learninghub_ft);

The FINETUNE command creates a new version of the openai_davinci model. You can query all available versions as below:

SELECT *
FROM models
WHERE name = 'openai_davinci';

While the model is being generated and trained, it is not active. The model becomes active only after it completes generating and training.

Once the new version status is complete and active, we can query the model again, expecting a more accurate output.

SELECT prompt, completion
FROM openai_davinci as m
WHERE prompt = 'What is the SQL syntax to join input data with predictions from a MindsDB machine learning model?'
USING max_tokens=400;

On execution, we get:

+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------+
| prompt                                                                                            | completion                                                                                           |
+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------+
| What is the SQL syntax to join input data with predictions from a MindsDB machine learning model? | SELECT * FROM mindsdb.models.my_model JOIN mindsdb.input_data_name;                                  |
+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------+

If you have dynamic data that gets updated regularly, you can set up an automated fine-tuning as below.

Note that the data source must contain an incremental column, such as timestamp or integer, so MindsDB can pick up only the recently added data with the help of the LAST keyword.

Here is how to create and schedule a job to fine-tune the model periodically.

CREATE JOB automated_finetuning (

    FINETUNE openai_davinci
    FROM mindsdb
        (SELECT *
        FROM files.openai_learninghub_ft
        WHERE timestamp > LAST)
)
EVERY 1 day
IF (
    SELECT *
    FROM files.openai_learninghub_ft
    WHERE timestamp > LAST
);

Now your model will be fine-tuned with newly added data every day or every time there is new data available.

Use Cases