OpenAI Models Fine-Tuning
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
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.
Here is a table we’ll use to fine-tune our model:
SELECT prompt, completion
FROM files.openai_learninghub_ft;
And here is its content:
+---------------------------------------------------------------------------------------------------+------------------------------------------+
| prompt | completion |
+---------------------------------------------------------------------------------------------------+------------------------------------------+
| What is the SQL syntax to connect a database to MindsDB? | CREATE DATABASE datasource_name |
| | [WITH] [ENGINE [=] engine_name] [,] |
| | [PARAMETERS [=] { |
| | "key": "value", |
| | ... |
| | }]; |
| | |
| What is the SQL command to create a home rentals MindsDB machine learning model? | CREATE MODEL |
| | mindsdb.home_rentals_model |
| | FROM example_db |
| | (SELECT * FROM demo_data.home_rentals) |
| | PREDICT rental_price; |
| | |
| What is the SQL syntax to join input data with predictions from a MindsDB machine learning model? | SELECT t.column_name, p.column_name, ... |
| | FROM integration_name.table_name [AS] t |
| | JOIN project_name.model_name [AS] p; |
+---------------------------------------------------------------------------------------------------+------------------------------------------+
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_versions
WHERE name = 'openai_davinci';
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; |
+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------+