Explore

Student Lab Learning Guide: Building a Generative AI Model with Anaconda Python: Step 1 of your Project: Building the generative AI Model.

Our tooling for this activity:

Anaconda Python Distribution

Visual Studio Studio; create and run Jupyter Notebooks to build up our Code.

Your Project deliverable will be your ML Ops Model with some kind of UI. ChatGTP prompt with a command line. Or use FLASK Python API which gives you a webserver inside your PYTHON Program.

Introduction:

In this lab, we will explore how to build a generative AI model using Anaconda Python and train it on a text document. The goal is to generate new text that resembles the style and content of the original document. By following this guide, you will learn the necessary steps to set up your environment, preprocess the data, build the model, and train it.

Prerequisites:

Anaconda Python distribution installed on your machine. You can download it from the official Anaconda website (

https://www.anaconda.com/products/individual⁠

Lab Setup Instructions:

Step 1: Create a new Conda environment

Open the Anaconda Navigator or launch Anaconda Prompt and run the following command to create a new environment:

luaCopy code

conda create -n generative_model python=3.9

Activate the newly created environment:

Copy code

conda activate generative_model

Step 2: Install necessary libraries

We'll need several Python libraries for this lab. Install them by running the following commands:

Copy code

pip install torch

pip install transformers

Step 3: Prepare the training data

Create a new directory for your project and place the text document you want to train on in that directory.

Step 4: Preprocess the data

In this step, we'll preprocess the text data to prepare it for training. Create a new Python script, e.g., preprocess.py, and add the following code:

pythonCopy code

import re

input_file = "path_to_your_text_file.txt"

output_file = "preprocessed_data.txt"

# Read the input text file

with open(input_file, "r", encoding="utf-8") as file:

text = file.read()

# Perform any necessary preprocessing steps

# For example, you can remove special characters, punctuation, or perform tokenization

# Remove special characters and punctuation

text = re.sub(r"[^a-zA-Z0-9\s]", "", text)

# Tokenization (splitting text into words)

tokens = text.split()

# Save the preprocessed data

with open(output_file, "w", encoding="utf-8") as file:

file.write(" ".join(tokens))

Replace "path_to_your_text_file.txt" with the actual path to your text document. This script removes special characters and punctuation and performs tokenization. It saves the preprocessed data to a new file named "preprocessed_data.txt" in the same directory.

Step 5: Build the generative model

Create a new Python script, e.g., generative_model.py, and add the following code:

pythonCopy code

import torch

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the pre-trained GPT-2 model and tokenizer

model_name = 'gpt2' # Change to a different model if desired

model = GPT2LMHeadModel.from_pretrained(model_name)

tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# Load and tokenize the preprocessed data

data_file = "preprocessed_data.txt"

with open(data_file, "r", encoding="utf-8") as file:

data = file.read()

input_ids = tokenizer.encode(data, return_tensors="pt")

# Generate new text

generated_output = model.generate(input_ids, max_length=100, num_return_sequences=1)

# Decode and print the generated text

generated_text = tokenizer.decode(generated_output[0], skip_special_tokens=True)

print(generated_text)

Step 6: Training the model (Optional)

Training a generative AI model from scratch requires significant computational resources and time. However, if you're interested in training the model on your own data, you can explore the official Hugging Face documentation (

https://huggingface.co/transformers/training.html⁠

) for detailed instructions on fine-tuning models.

Conclusion:

In this lab, we learned how to set up Anaconda Python, preprocess text data, build a generative AI model using pre-trained models from Hugging Face's Transformers library, and generate new text. This is just a starting point, and you can further explore various techniques and architectures to enhance your generative AI model.

Remember to deactivate the conda environment once you have finished the lab:

Copy code

conda deactivate

Congratulations on completing the lab! You can now experiment with different models, data, and parameters to further develop your generative AI model. Keep exploring and enjoy your journey into the fascinating world of AI!

Want to print your doc?
This is not the way.

Try clicking the ⋯ next to your doc name or using a keyboard shortcut (

CtrlP

) instead.