Building a simple LLM in Python
To build a simple LLM in Python, we will use the following libraries:
- TensorFlow
- spaCy
- NLTK
Prerequisites
You will need to have Python 3 installed on your system. You can also install the necessary libraries using the following command:
1pip install tensorflow spacy nltkImporting the necessary libraries
First, we need to import the necessary libraries:
1import tensorflow as tf
2import spacy
3import nltkLoading and preprocessing the training data
Next, we need to load and preprocess the training data. We can use the nltk.corpus.gutenberg corpus to load a collection of English books. We can then use the spaCy library to preprocess the text, such as tokenizing it and removing stop words.
1# Load the training data
2gutenberg_corpus = nltk.corpus.gutenberg.raw(fileids=['austen-emma.txt', 'austen-persuasion.txt'])
3
4# Preprocess the text
5nlp = spacy.load('en_core_web_sm')
6preprocessed_text = [nlp(doc.strip()) for doc in gutenberg_corpus.split('\n')]Defining the LLM architecture
We will use a simple recurrent neural network (RNN) architecture for our LLM. The RNN will be trained to predict the next word in a sequence, given the previous words in the sequence.
1class LLM(tf.keras.Model):
2 def __init__(self, vocabulary_size, embedding_dim, hidden_dim):
3 super(LLM, self).__init__()
4
5 # Embedding layer
6 self.embedding_layer = tf.keras.layers.Embedding(vocabulary_size, embedding_dim)
7
8 # RNN layer
9 self.rnn_layer = tf.keras.layers.LSTM(hidden_dim)
10
11 # Dense layer
12 self.dense_layer = tf.keras.layers.Dense(vocabulary_size)
13
14 def call(self, inputs):
15 embeddings = self.embedding_layer(inputs)
16 rnn_output = self.rnn_layer(embeddings)
17 predictions = self.dense_layer(rnn_output)
18
19 return predictionsTo use a transformer architecture instead of RNN in the Python example, we can use the following code:
1import tensorflow as tf
2from transformers import TFLongformerModel
3
4class LLM(tf.keras.Model):
5 def __init__(self, vocabulary_size, embedding_dim, hidden_dim):
6 super(LLM, self).__init__()
7
8 # Embedding layer
9 self.embedding_layer = tf.keras.layers.Embedding(vocabulary_size, embedding_dim)
10
11 # Transformer encoder
12 self.transformer_encoder = TFLongformerModel.from_pretrained('allenai/longformer-base-4096')
13
14 # Dense layer
15 self.dense_layer = tf.keras.layers.Dense(vocabulary_size)
16
17 def call(self, inputs):
18 embeddings = self.embedding_layer(inputs)
19 transformer_output = self.transformer_encoder(embeddings)
20 predictions = self.dense_layer(transformer_output[0])
21
22 return predictions
23
24# Create a transformer LLM
25transformer_model = LLM(len(vocabulary), 128, 256)
26
27# Compile the model
28transformer_model.compile(loss='categorical_crossentropy', optimizer='adam')The main difference between the RNN and transformer models is that the transformer model uses a transformer encoder instead of an RNN layer. The transformer encoder is able to learn long-range dependencies in text, which can improve the performance of the model on tasks such as machine translation and text summarization.
Another difference is that the transformer model uses a different embedding layer than the RNN model. The transformer model uses a positional encoding embedding layer, which allows the model to encode the position of each word in the sequence. This can be helpful for tasks such as question answering, where the model needs to understand the context of the question in order to answer it accurately.
Overall, the transformer model is a more powerful and versatile model than the RNN model. However, it is also more computationally expensive to train.
