In our previous blog “All you need to know about Qwen2.5“, we explored how this open-source language model is nearly on par with GPT-4. As models like Qwen2.5 are built on the Transformer architecture, it’s crucial to understand how this groundbreaking technology works.

Transformers, a groundbreaking concept in Natural Language Processing (NLP), have revolutionized our approach to language comprehension. This innovative idea was first introduced in the seminal paper “Attention is All You Need” by Vaswani et al. Unlike the sequential processing of Recurrent Neural Networks (RNNs), Transformers employ an ‘attention’ mechanism that assigns significance to words in a sentence, thereby capturing context more effectively. This architecture forms the backbone of state-of-the-art models like BERT, GPT, and T5, which have set new benchmarks in tasks such as text generation, text classification, sentiment analysis, and machine translation.

Before we delve into the intricate details of the transformer architecture, let’s draw an analogy that simplifies the understanding of how transformers operate.

Imagine you are trying to comprehend a lengthy story. You can’t simply read the story from beginning to end, as you will forget what happened earlier in the narrative. Instead, you need to be able to focus on the important parts of the story and ignore the unimportant ones. Transformers function in a similar way. They can concentrate on the crucial elements of a sequence and disregard the less significant ones, even if the important parts are not consecutive.

Now, let’s delve into the detailed explanation of the Transformer model.

The Transformer model is based on the concept of self-attention (also known as scaled dot-product attention or simply attention), which allows the model to weigh the importance of different words in a sentence when generating an output. when you break it down into its most important parts, it’s not so bad. The transformer has 4 main parts:

• Tokenization
• Embedding
• Positional encoding
• Transformer block (several of these)
• Softmax

1. Tokenization

Tokenization is the first step in processing input text within the Transformer model. It involves breaking down a sentence into smaller units called tokens, which are typically words, subwords, or characters. These tokens serve as the basic building blocks that the model processes.

Example: Imagine you have the sentence “The quick brown fox.” During tokenization, this sentence might be split into individual words like [“The”, “quick”, “brown”, “fox”]. In more complex tokenization schemes, words might be broken down further into subwords, especially for handling rare or compound words. For example, the word “unbelievable” might be tokenized into [“un”, “believ”, “able”].

Tokenization is crucial because it converts raw text into a format that the model can understand and process, setting the stage for further transformations.

2. Embedding

Once the text has been tokenized, the next step is embedding. Embedding transforms tokens into vectors—numerical representations that capture the semantic meaning of the words. These vectors are typically high-dimensional, meaning they contain a lot of information about the relationships between words.

Example: Consider the words “king” and “queen.” In a well-trained embedding space, these two words will have vectors that are close to each other, reflecting their similar meanings. If you were to visualize this in a simple 2D space, “king” and “queen” might be represented as points that are near each other, but with slight differences that capture their unique aspects (like gender).

Embeddings allow the Transformer to work with numerical data, which is necessary for all subsequent computations. More importantly, they capture the nuances of language, such as word similarity and context, which are critical for understanding and generating natural language.

3. Positional Encoding

While embeddings convert tokens into numerical vectors, they do not inherently capture the order of words in a sentence. This is where positional encoding comes in. Since Transformers process words in parallel rather than sequentially, they need a way to understand the position of each word in the sequence.

Positional encoding adds unique numerical information to each token’s embedding, indicating its position in the sentence. This allows the Transformer to take into account the order of words, which is crucial for meaning.

Example: In the sentence “The quick brown fox,” the word “The” would have a positional encoding indicating it is the first word, “quick” would have an encoding indicating it is the second word, and so on. If you swapped the positions of “quick” and “brown,” the positional encodings would change accordingly, helping the model understand that the meaning of the sentence has changed.

This ensures that the Transformer knows not just what the words are, but also the sequence in which they appear, preserving the structure of the original sentence.

4. Transformer Block

The core of the Transformer model is the Transformer block, a modular unit that can be stacked to build deeper models. Each block is composed of two main sub-layers: Multi-Head Self-Attention and Feed-Forward Neural Networks.

a) Multi-Head Self-Attention

Self-attention is the mechanism that allows the Transformer to weigh the importance of different words in a sentence relative to each other. “Multi-head” means that the model can focus on multiple aspects of the sentence simultaneously, capturing different types of relationships between words.

Example: When processing the sentence “The quick brown fox jumps over the lazy dog,” the self-attention mechanism might identify that “fox” is closely related to “jumps,” while “lazy” is more related to “dog.” By having multiple attention heads, the Transformer can simultaneously consider the relationship between “fox” and “jumps” and the relationship between “lazy” and “dog,” leading to a more nuanced understanding of the sentence.

b) Feed-Forward Neural Networks

After self-attention, the output passes through a feed-forward neural network, which applies further transformations to the data. This network typically consists of two linear layers with a ReLU activation in between, adding non-linearity to the model, which helps in capturing complex patterns.

Example: Think of the feed-forward network as a refinement stage. After self-attention highlights important relationships, the feed-forward network further processes these insights, allowing the model to better understand and generate language.

Together, these components make the Transformer block a powerful tool for processing language. By stacking multiple Transformer blocks, the model can capture increasingly complex patterns and relationships, enabling it to handle a wide range of NLP tasks effectively.

5. Softmax

The final step in the Transformer model is the application of the softmax function. Softmax is used to convert the model’s outputs into probabilities, which represent the likelihood of each possible outcome.

Example: If the Transformer is used for text generation, the softmax function will assign probabilities to each word in the vocabulary, indicating how likely each word is to follow the current sequence. For instance, if the model is predicting the next word in “The quick brown,” it might assign high probabilities to words like “fox” or “bear,” and low probabilities to unrelated words like “computer” or “apple.”

The word with the highest probability is then selected as the model’s output, or in some cases, sampling techniques might be used to introduce some randomness into the selection, leading to more varied and creative outputs.

Post Training

The transformer is not a human who thinks about their responses, it simply mimics what it sees on the internet (or any dataset that has been provided). So how do we get the transformer to answer questions?

The answer is post-training. In the same way that you would teach a person to do certain tasks, you can get a transformer to perform tasks. Once a transformer is trained on the entire internet, then it is trained again on a large dataset which corresponds to lots of questions and their respective answers. Transformers (like humans), have a bias towards the last things they’ve learned, so post-training has proven a very useful step to help transformers succeed at the tasks they are asked to.

Post-training also helps with many other tasks. For example, one can post-train a transformer with large datasets of conversations, in order to help it perform well as a chatbot, or to help us write stories, poems, or even code.

Effective Use Cases for Transformer Models

Transformer models have revolutionized various fields, particularly in natural language processing (NLP), but their applications extend far beyond this domain. Below are some of the most effective use cases for transformer models across different areas:

Natural Language Processing (NLP)

1. Machine Translation: Transformers excel in translating text from one language to another, leveraging their ability to understand context and relationships between words. The encoder-decoder architecture is particularly effective for this task, allowing for nuanced translations that consider the entire input sequence.
2. Text Summarization: These models can generate concise summaries of longer texts by capturing key points and maintaining coherence, making them valuable for news articles, research papers, and other lengthy documents.
3. Sentiment Analysis: Transformers are widely used in analyzing customer feedback and social media posts to gauge public sentiment toward products or services. They classify emotions and sentiments effectively, providing insights for businesses.
4. Question Answering: Encoder-only transformers are adept at understanding context and providing accurate answers to questions based on a given text, making them useful in educational tools and customer service applications.
5. Chatbots and Conversational Agents: By utilizing autoregressive transformers, chatbots can generate contextually relevant responses in real-time conversations, enhancing user engagement and support experiences.

Computer Vision

1. Image Classification: Vision Transformers (ViTs) have emerged as strong contenders in image classification tasks, utilizing self-attention mechanisms to analyze visual data effectively.
2. Object Detection and Segmentation: Transformers can identify and segment objects within images, significantly improving performance in tasks such as autonomous driving and surveillance systems.
3. Image Generation: Combining transformers with diffusion models allows for the generation of images based on textual prompts, showcasing their versatility in creative applications.

Reinforcement Learning

1. Decision Transformers: This innovative approach integrates transformer architectures with reinforcement learning, enabling agents to learn from past experiences without requiring extensive online training. This method is particularly beneficial in environments like robotics and gaming.
2. Long-term Dependency Handling: Transformers’ ability to manage long-term dependencies makes them suitable for complex decision-making tasks where actions are interconnected over time, enhancing strategic planning capabilities.

Conclusion

Transformer models have revolutionized the field of machine learning and artificial intelligence, driving innovation across various domains. Their unique architecture, based on self-attention mechanisms, has enabled significant advancements in natural language processing, computer vision, reinforcement learning, and beyond.

In NLP, transformers have set new benchmarks in tasks like machine translation, text summarization, and question answering. Models like BERT and GPT have demonstrated remarkable language understanding and generation capabilities, paving the way for more sophisticated AI applications such as virtual assistants and automated content creation.

Beyond NLP, transformers have made inroads into computer vision, with Vision Transformers (ViT) and DETR emerging as strong competitors in image classification and object detection. The combination of transformers with diffusion models has also enabled the generation of images from textual descriptions, showcasing the versatility of this architecture in multimodal AI systems.In reinforcement learning, transformers have proven to be a game-changer. Decision Transformers leverage the strengths of transformers to enable offline RL, reducing the need for resource-intensive online training.

They also excel at handling long-term dependencies and generating future action sequences to optimize reward outcomes, making them suitable for applications like robotics, autonomous vehicles, and strategic gameplay.The adaptability and scalability of transformers have been key factors in their widespread adoption. Techniques like transfer learning and retrieval augmented generation (RAG) enable the customization of existing models for specific applications, democratizing the use of sophisticated models and removing resource constraints.As the field of AI continues to evolve, transformers are poised to play an increasingly crucial role in driving innovation and pushing the boundaries of what’s possible in machine learning.

Their ability to process complex data, understand context, and generate human-like outputs positions them as a must-know technology for anyone interested in the future of artificial intelligence.