Finetune OpenAI / LLM / Hugging Face model with your own data
This article will guide you through the process of fine-tuning OpenAI's GPT-4 or Hugging Face models with own data and then generating insights.
Introduction
Fine-tuning pre-trained models like GPT-4 or Hugging Face models allows us to leverage the power of these models and adapt them to specific tasks. This process involves training the model on a specific dataset, in this case, 1000 SQL tables, to adjust the model parameters and improve its performance on the task at hand.
Fine-tuning GPT-4 or Hugging Face Models
Fine-tuning involves a few steps, including data preparation, model configuration, training, and evaluation. Here's a general outline of the process:
1. Data Preparation
This is the first and one of the most crucial steps in the process. It involves loading your SQL tables and preparing them for the model. You'll need to convert your data into a format that the model can understand. For GPT-4 or Hugging Face models, this typically involves tokenization, where the data is broken down into smaller pieces, or tokens.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import pandas as pd
# Load the fine-tuned model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/tapex-large-finetuned-wtq") In the above code, we first import the necessary libraries. We then load the model and tokenizer. The tokenizer will be used to convert our data into a format that the model can understand.
# Prepare your SQL table and question
data = {"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]}
table = pd.DataFrame.from_dict(data)
question = "how many movies does Leonardo Di Caprio have?"
Here, we prepare our SQL table and the question we want to ask. The table is created using a dictionary, which is then converted into a pandas DataFrame. The question is a simple string.
# Encode the table and question
encoding = tokenizer(table, question, return_tensors="pt")Finally, we encode the table and question using the tokenizer. The return_tensors="pt" argument tells the tokenizer to return PyTorch tensors.
2. Model Configuration
Next, you'll need to configure your model for the task. This involves setting various parameters, such as the learning rate, batch size, and number of training epochs. For the sake of simplicity, we'll skip this step in our code example, but it's an important part of the fine-tuning process.
3. Training
During training, the model learns from your data. It adjusts its internal parameters to minimize the difference between its predictions and the actual values. This step is also not shown in the code example, but it would involve calling a method like model.fit() or model.train() and passing in your data and configuration parameters.
4. Evaluation
After training, you'll want to evaluate your model to see how well it performs. This typically involves running the model on a separate test dataset and comparing the model's predictions to the actual values.
Generating Insights
Once your model is fine-tuned, you can use it to generate insights from your data. This could involve various tasks, such as predicting future trends, identifying patterns, or answering specific questions about the data.
# Generate an answer
outputs = model.generate(**encoding)In this line of code, we generate an answer from the model by calling the model.generate() method and passing in our encoded table and question.
# Decode the answer
predicted_answer = tokenizer.batch_decode(outputs,
skip_special_tokens=True)[0]
print(predicted_answer)
Finally, we decode the answer using the tokenizer's batch_decode() method and print it out. The skip_special_tokens=True argument tells the tokenizer to ignore special tokens, such as padding tokens, when decoding.
Conclusion
Fine-tuning models like GPT-4 or Hugging Face models with SQL tables can be a powerful way to generate insights from your data. By following the steps outlined in this article, you can leverage these models for your specific tasks and improve your AI and data engineering skills. Remember, the key to successful fine-tuning is understanding your data, configuring your model correctly, and evaluating its performance thoroughly.