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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.
Once you have your text data, you'll need to preprocess it. This typically involves:
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.
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.
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
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:
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)
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