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Building a Simple AI Generative Language Model in Python

Building a Simple AI Generative Language Model in Python

This Lab Workbook will walk you through the process of creating a simple AI generative language model using Python. (Which is your Project: Here is the Blueprint).
We will use Google Colab as our development environment and leverage well-supported libraries such as TensorFlow and PyTorch.
The model will be trained on a document of your choice, and we will demonstrate how to hold conversations with the trained model.

Step 1: Setting Up the Environment

Google Colab is a hosted Jupyter Notebook service that requires no setup and provides free access to computing resources, including GPUs and TPUs. It is especially well suited to machine learning, data science, and education
.
To start, create a new Python notebook in Google Colab.

Step 2: Importing Necessary Libraries

We will use TensorFlow and PyTorch, popular AI frameworks used for developing machine learning models.
These frameworks provide a comprehensive set of tools that enable developers to easily create and deploy ML models
.

Here's how to import these libraries in your Colab notebook:

import tensorflow as tf
import torch

Step 3: Gathering and Preprocessing Data

The first step in building a language model is to gather and preprocess the data.
The data for a language model is typically a large corpus of text.
For example, you could use a book, a collection of articles, or any other large text file
.

Once you have your text data, you'll need to preprocess it. This typically involves:
Tokenization: Splitting the text into individual words or tokens.
Lowercasing: Converting all the text to lowercase to ensure the model doesn't treat the same word in different cases as different words.
Removing punctuation and non-alphanumeric characters: This simplifies the model's input space.
Here's a simple example of how you might preprocess your data:

import re

def preprocess_text(text):
text = text.lower()
text = re.sub(r'\d+', '', text)
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'\W', ' ', text)
return text

Step 4: Building the Model

We will use a Recurrent Neural Network (RNN) for our language model.
RNNs are great for generating sequences, like sentences or melodies
.
Here's a simple example of how you might define an RNN in PyTorch:

class RNNModel(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
super(RNNModel, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.rnn = nn.RNN(embed_size, hidden_size, num_layers)
self.linear = nn.Linear(hidden_size, vocab_size)
def forward(self, x, h):
x = self.embed(x)
out, h = self.rnn(x, h)
out = self.linear(out)
return out, h

To resolve the NameError and use the nn module in your class definition, you should import torch.nn, which is the neural networks module from the PyTorch library.
Using import re alone won't help with defining the neural network, as re is the regular expression library in Python.
Here's how you can fix the error:
import torch import torch.nn as nn
class RNNModel(nn.Module): def __init__(self, vocab_size, embed_size, hidden_size, num_layers): super(RNNModel, self).__init__() self.embed = nn.Embedding(vocab_size, embed_size) self.rnn = nn.RNN(embed_size, hidden_size, num_layers, batch_first=True) self.linear = nn.Linear(hidden_size, vocab_size) def forward(self, x, h): x = self.embed(x) out, h = self.rnn(x, h) out = self.linear(out.reshape(out.size(0)*out.size(1), out.size(2))) return out, h
In this corrected code:
torch is imported to ensure we have access to all necessary PyTorch functions and classes.
torch.nn is aliased as nn for ease of use.
The RNNModel class extends nn.Module, which is the base class for all neural network modules in PyTorch.
The forward method is where the input tensor x goes through the layers of the network.
To actually run this code in your Google Colab environment, you'll need to install PyTorch if it's not already available in your session, although Colab typically comes with PyTorch pre-installed. Here's how you can check and install PyTorch if necessary:
!pip install torch
After importing the necessary libraries and defining your model, you'll be ready to instantiate the RNNModel class and use it for whatever task you have in mind, such as text generation or another sequence modeling task.
import torch import torch.nn as nn
class RNNModel(nn.Module): def __init__(self, vocab_size, embed_size, hidden_size, num_layers): super(RNNModel, self).__init__() self.embed = nn.Embedding(vocab_size, embed_size) self.rnn = nn.RNN(embed_size, hidden_size, num_layers, batch_first=True) self.linear = nn.Linear(hidden_size, vocab_size) def forward(self, x, h): x = self.embed(x) out, h = self.rnn(x, h) out = self.linear(out.reshape(out.size(0)*out.size(1), out.size(2))) return out, h

Step 5: Training the Model

Training involves feeding your preprocessed data into the model, calculating the error of the model's predictions, and updating the model's parameters to reduce this error.
This process is repeated for a number of iterations or epochs
.
Here's a simple example of a training loop in PyTorch:

def train(model, data, epochs, lr):
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
criterion = nn.CrossEntropyLoss()
for epoch in range(epochs):
hidden = None
for x, y in data:
optimizer.zero_grad()
outputs, hidden = model(x, hidden)
loss = criterion(outputs, y)
loss.backward()
optimizer.step()

Step 6: Using the Model for Inference [Doing the next token generation]

Once the model is trained, you can use it to generate new text.
This involves:
Providing the model with a seed sequence
Having the model make a prediction for the next word
Adding the predicted word to the sequence
Repeating this process for as many words as you want to generate
.
Here's a simple example of how you might generate new text with your model:

Explaindef generate_text(model, seed_text, num_words):
model.eval()
text = seed_text
for _ in range(num_words):
x = torch.tensor([text[-1]])
output, _ = model(x, None)
_, predicted = torch.max(output, 1)
text.append(predicted.item())
return text

Step 7: Interacting with the Model

You will interact with your trained model by providing it with a seed sequence {prompt engineering} and having it generate a response. This can be done in a loop to simulate a conversation with the model
.
The quality of the generated text will depend on the complexity of your model and the amount and quality of the training data.
More complex models trained on larger and more diverse datasets will generally produce better results.
This Lab provides a basic introduction to building a simple AI generative language model in Python.
There are many ways to expand on this, such as using more complex models, incorporating additional features into your model, or using more advanced training techniques.

What are the basic steps to build a generative language model in python


Sources

Create a Large Language Model from Scratch with Python – Tutorial - YouTube


Building a generative language model in Python involves several steps, including setting up the environment, importing necessary libraries, gathering and preprocessing data, building the model, training the model, using the model for inference, and interacting with the model. Here's a step-by-step guide:

Step 1: Setting Up the Environment

You can use Google Colab, a hosted Jupyter Notebook service that requires no setup and provides free access to computing resources, including GPUs and TPUs
.

Step 2: Importing Necessary Libraries

You will need to import TensorFlow and PyTorch, which are popular AI frameworks used for developing machine learning models
.
python
import tensorflow as tf
import torch

Step 3: Gathering and Preprocessing Data

The first step in building a language model is to gather and preprocess the data. The data for a language model is typically a large corpus of text. Preprocessing typically involves tokenization, lowercasing, and removing punctuation and non-alphanumeric characters
.
python
Explainimport re

def preprocess_text(text):
text = text.lower()
text = re.sub(r'\d+', '', text)
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'\W', ' ', text)
return text

Step 4: Building the Model

You can use a Recurrent Neural Network (RNN) for your language model. RNNs are great for generating sequences, like sentences or melodies
.
python
Explainclass RNNModel(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
super(RNNModel, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.rnn = nn.RNN(embed_size, hidden_size, num_layers)
self.linear = nn.Linear(hidden_size, vocab_size)
def forward(self, x, h):
x = self.embed(x)
out, h = self.rnn(x, h)
out = self.linear(out)
return out, h

Step 5: Training the Model

Training involves feeding your preprocessed data into the model, calculating the error of the model's predictions, and updating the model's parameters to reduce this error. This process is repeated for a number of iterations or epochs
.
python
Explaindef train(model, data, epochs, lr):
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
criterion = nn.CrossEntropyLoss()
for epoch in range(epochs):
hidden = None
for x, y in data:
optimizer.zero_grad()
outputs, hidden = model(x, hidden)
loss = criterion(outputs, y)
loss.backward()
optimizer.step()

Step 6: Using the Model for Inference

Once the model is trained, you can use it to generate new text. This involves providing the model with a seed sequence, having the model make a prediction for the next word, adding the predicted word to the sequence, and repeating this process for as many words as you want to generate
.
python
def generate_text(model, seed_text, num_words):
model.eval()
text = seed_text
for _ in range(num_words):
x = torch.tensor([text[-1]])
output, _ = model(x, None)
_, predicted = torch.max(output, 1)
text.append(predicted.item())
return text

Step 7: Interacting with the Model

You can interact with your trained model by providing it with a seed sequence and having it generate a response. This can be done in a loop to simulate a conversation with the model
. The quality of the generated text will depend on the complexity of your model and the amount and quality of the training data
.

Building a Simple AI Generative Language Model in Python

This guide will walk you through the process of creating a simple AI generative language model using Python. We will use Google Colab as our development environment and leverage well-supported libraries such as TensorFlow and PyTorch. The model will be trained on a document of your choice, and we will demonstrate how to hold conversations with the trained model.

Step 1: Setting Up the Environment

Google Colab is a hosted Jupyter Notebook service that requires no setup and provides free access to computing resources, including GPUs and TPUs. It is especially well suited to machine learning, data science, and education
.
To start, create a new Python notebook in Google Colab.

Step 2: Importing Necessary Libraries

We will use TensorFlow and PyTorch, popular AI frameworks used for developing machine learning models. These frameworks provide a comprehensive set of tools that enable developers to easily create and deploy ML models
.
Here's how to import these libraries in your Colab notebook:
python
import tensorflow as tf
import torch

Step 3: Gathering and Preprocessing Data

The first step in building a language model is to gather and preprocess the data. The data for a language model is typically a large corpus of text. For example, you could use a book, a collection of articles, or any other large text file
.
Once you have your text data, you'll need to preprocess it. This typically involves:
Tokenization: Splitting the text into individual words or tokens.
Lowercasing: Converting all the text to lowercase to ensure the model doesn't treat the same word in different cases as different words.
Removing punctuation and non-alphanumeric characters: This simplifies the model's input space.
Here's a simple example of how you might preprocess your data:
python
Explainimport re

def preprocess_text(text):
text = text.lower()
text = re.sub(r'\d+', '', text)
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'\W', ' ', text)
return text

Step 4: Building the Model

We will use a Recurrent Neural Network (RNN) for our language model. RNNs are great for generating sequences, like sentences or melodies
.
Here's a simple example of how you might define an RNN in PyTorch:
python
Explainclass RNNModel(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
super(RNNModel, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.rnn = nn.RNN(embed_size, hidden_size, num_layers)
self.linear = nn.Linear(hidden_size, vocab_size)
def forward(self, x, h):
x = self.embed(x)
out, h = self.rnn(x, h)
out = self.linear(out)
return out, h

Step 5: Training the Model

Training involves feeding your preprocessed data into the model, calculating the error of the model's predictions, and updating the model's parameters to reduce this error. This process is repeated for a number of iterations or epochs
.
Here's a simple example of a training loop in PyTorch:
python
Explaindef train(model, data, epochs, lr):
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
criterion = nn.CrossEntropyLoss()
for epoch in range(epochs):
hidden = None
for x, y in data:
optimizer.zero_grad()
outputs, hidden = model(x, hidden)
loss = criterion(outputs, y)
loss.backward()
optimizer.step()

Step 6: Using the Model for Inference

Once the model is trained, you can use it to generate new text. This involves providing the model with a seed sequence, having the model make a prediction for the next word, adding the predicted word to the sequence, and repeating this process for as many words as you want to generate
.
Here's a simple example of how you might generate new text with your model:
python
Explaindef generate_text(model, seed_text, num_words):
model.eval()
text = seed_text
for _ in range(num_words):
x = torch.tensor([text[-1]])
output, _ = model(x, None)
_, predicted = torch.max(output, 1)
text.append(predicted.item())
return text

Step 7: Interacting with the Model

You can interact with your trained model by providing it with a seed sequence and having it generate a response. This can be done in a loop to simulate a conversation with the model
.
Remember, the quality of the generated text will depend on the complexity of your model and the amount and quality of the training data. More complex models trained on larger and more diverse datasets will generally produce better results.
This guide provides a basic introduction to building a simple AI generative language model in Python. There are many ways to expand on this, such as using more complex models, incorporating additional features into your model, or using more advanced training techniques.


Simple learning guide on creating and training a generative language model using Python, focusing on Transformers-based library called GPT-2. This model has been well noted for its ability in generating coherent and contextually relevant sentences based on a given prompt.
Prerequisites:
Ensure you have a Google account to access Google Colab, a free, online Jupyter notebook that requires no setup. It also comes with free GPU access.
Step 1: Setting Up the Environment
To start, open Google Colab and create a new Python3 notebook.
Step 2: Importing the Necessary Libraries
Here, we'll use the Transformers library by Hugging Face which gives us a straightforward interface for working with models such as GPT-2.
Install it by running this cell of code:
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!pip install transformers

Open in: Code Editor
Step 3: Importing the Model and Tokenizer
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from transformers import GPT2LMHeadModel, GPT2Tokenizer

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

Open in: Code Editor
Here, we're importing a pre-trained GPT2 model and its respective tokenizer.
Step 4: Encoding and Decoding Functions
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def encode(prompt):
return tokenizer.encode(prompt, return_tensors="pt")

def decode(encoded_prompt):
return tokenizer.decode(encoded_prompt[0], skip_special_tokens=True)

Open in: Code Editor
Step 5: Running The Model
Now for the fun part: generating text!
Inputs and outputs to the GPT2 model are all sequences of integers. We can encode our input prompt, generate a response, and then decode this response to get our output message:
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input_prompt = "How are you feeling today?"

input_prompt_encoded = encode(input_prompt)
output = model.generate(input_prompt_encoded, max_length=50, num_return_sequences=1, no_repeat_ngram_size=2, do_sample=True)
output_message = decode(output)

print(output_message)

Open in: Code Editor
Step 6: Create a conversational model
For creating a conversational model, one needs to append the prompt to all previous dialogue.
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# conversation history
history_encoded = tokenizer.encode("Hello, I'm an AI model. ", return_tensors="pt")

# user input
user_input_encoded = tokenizer.encode("Hello, how are you?", return_tensors="pt")

# append the new user input tokens to the chat history
history_with_user_input_encoded = torch.cat([history_encoded, user_input_encoded], dim=-1)

# generate a response
output = model.generate(history_with_user_input_encoded, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2, do_sample=True)

history_with_reply_encoded = output

# Print message
output_message = decode(history_with_reply_encoded)
print(output_message)

Open in: Code Editor
Follow the same for other user inputs. Always append the user message and generated message to the history_with_reply_encoded every time a new user input is there.
That's it! Your basic AI conversational model is ready to play with.
Remember, this is only a rough guide, and actual results will vary based on the query and the GPT-2 model training. Take this as a starting point and tinker with different parameters and functionalities to get the most out of GPT-2.
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