AlgoDaily - How Do Large Language Models Work?

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Attached is the final code to play around with:

PYTHON

1import tensorflow as tf
2import spacy
3import nltk
4from transformers import TFLongformerModel
5
6# Load the training data
7gutenberg_corpus = nltk.corpus.gutenberg.raw(fileids=['austen-emma.txt', 'austen-persuasion.txt'])
8
9# Preprocess the text
10nlp = spacy.load('en_core_web_sm')
11preprocessed_text = [nlp(doc.strip()) for doc in gutenberg_corpus.split('\n')]
12
13# Create a vocabulary
14vocabulary = {}
15for doc in preprocessed_text:
16    for token in doc:
17        if token.text not in vocabulary:
18            vocabulary[token.text] = len(vocabulary)
19
20# Create a training dataset
21training_dataset = tf.data.Dataset.from_tensor_slices(preprocessed_text)
22training_dataset = training_dataset.batch(64)
23
24# Define the transformer LLM model
25class LLM(tf.keras.Model):
26    def __init__(self, vocabulary_size, embedding_dim, hidden_dim):
27        super(LLM, self).__init__()
28
29        # Embedding layer
30        self.embedding_layer = tf.keras.layers.Embedding(vocabulary_size, embedding_dim)
31
32        # Transformer encoder
33        self.transformer_encoder = TFLongformerModel.from_pretrained('allenai/longformer-base-4096')
34
35        # Dense layer
36        self.dense_layer = tf.keras.layers.Dense(vocabulary_size)
37
38    def call(self, inputs):
39        embeddings = self.embedding_layer(inputs)
40        transformer_output = self.transformer_encoder(embeddings)
41        predictions = self.dense_layer(transformer_output[0])
42
43        return predictions
44
45# Create the model
46model = LLM(len(vocabulary), 128, 256)
47
48# Compile the model
49model.compile(loss='categorical_crossentropy', optimizer='adam')
50
51# Train the model
52model.fit(training_dataset, epochs=10)
53
54# Generate text from the model
55generated_text = model.predict(tf.constant([vocabulary['the']], dtype=tf.int32))
56generated_text = vocabulary[tf.argmax(generated_text, axis=1)[0]]
57
58# Print the generated text
59print('Generated text:', generated_text)

To run this code, you will need to have Python 3 and TensorFlow installed. You can install TensorFlow with the following command:

SNIPPET

1pip install tensorflow

Once you have TensorFlow installed, you can run the code by saving it as a Python file (e.g. llm.py) and running the following command:

SNIPPET

1python llm.py

This will train the LLM model on the Gutenberg corpus and generate some text from the model.

In this tutorial, we learned about the basics of large language models (LLMs) and how to build a simple LLM in Python. We also discussed different LLM architectures, transfer learning, and fine-tuning.

LLMs are a powerful new technology with a wide range of potential applications. However, it is important to be aware of the limitations of LLMs and to use them responsibly.

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