What to Expect

This script demonstrates how to use the Instructor library for fine-tuning a Python function that performs three-digit multiplication. It uses Pydantic for type validation and logging features to generate a fine-tuning dataset.

How to Run

Prerequisites

Python 3.9
Instructor library

Steps

Install Dependencies
If you haven't already installed the required libraries, you can do so using pip:
```
pip install instructor pydantic
```
Set Up Logging
The script uses Python's built-in logging module to log the fine-tuning process. Ensure you have write permissions in the directory where the log file math_finetunes.jsonl will be saved.
Run the Script
Navigate to the directory containing script.py and run it:
```
python three_digit_mul.py
```
This will execute the script, running the function ten times with random three-digit numbers for multiplication. The function outputs and logs are saved in math_finetunes.jsonl.
Fine-Tuning
Once you have the log file, you can run a fine-tuning job using the following Instructor CLI command:
```
instructor jobs create-from-file math_finetunes.jsonl
```
Wait for the fine-tuning job to complete.

If you have validation date you can run:
```
instructor jobs create-from-file math_finetunes.jsonl --n-epochs 4 --validation-file math_finetunes_val.jsonl 
```

Output

That's it! You've successfully run the script and can now proceed to fine-tune your model.

Dispatch

Once you have the model you can replace the model in three_digit_mul_dispatch.py with the model you just fine-tuned and run the script again. This time, the script will use the fine-tuned model to predict the output of the function.