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.


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.
Anaconda Python distribution installed on your machine. You can download it from the official Anaconda website ().
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:
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conda create -n generative_model python=3.9

Activate the newly created environment:
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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:
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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.,, and add the following code:
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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 =

# 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.,, and add the following code:
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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 =

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)

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 () for detailed instructions on fine-tuning models.
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:
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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!
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