Transformers: Simple Yet Wildly Versatile

When the Transformer arrived in 2017, it was built for one thing: sequence-to-sequence translation. A clean, attention-only neural architecture that skipped recurrence and convolutions, yet outperformed state-of-the-art machine translation. The original paper, [*Attention Is All You Need*](https://arxiv.org/abs/1706.03762), turned out to be the gateway to much more than translation. Fast forward to today, and that same blueprint is everywhere. It writes essays, fixes code, understands images, and even conjures photo-realistic media—images, audio, video—from pure noise. The leap wasn’t about tearing up the blueprint and starting over. It was about **small architectural tweaks, combined with clever retraining on new data**. Each reinterpretation of what counts as a “token” stretched the Transformer into a new world. --- ## Language First: Next-Token Prediction At its core, a Transformer is a probabilistic machine: given a sequence, it predicts what comes next. “Deep learning is…” → “transforming”, “changing”, “everywhere.” The breakthrough was **self-attention**, letting each word weigh every other word. Unlike RNNs, which only remembered the recent past, Transformers could see the whole sentence at once. From this base came influential variants: * **BERT (2018)** for masked-language pretraining, enabling stronger understanding. * **RoBERTa (2019)** for better scaling and training. * **T5 (2019)** reframing every NLP task as text-to-text. These tweaks turned the Transformer from a translation engine into the foundation for today’s language models.  --- ## Seeing: Vision Transformers (ViTs) The next leap was deceptively simple: treat an image like text. Slice it into small **patches** (say, 16×16 pixels), embed each as a token, and feed the sequence into a Transformer. Suddenly, the model isn’t reading words—it’s **reading images**. That’s the Vision Transformer (ViT). No convolutions, no hand-crafted filters. Just pure attention. Soon, models like [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) and [OpenAI’s CLIP](https://huggingface.co/openai/clip-vit-base-patch32) proved that this approach could rival, then surpass, convolutional networks.  --- ## Coding: Transformers Learn to Program Code became the next frontier. And here, the tweak was almost nonexistent. Train the same architecture, but on GitHub code instead of novels. Suddenly, predicting the next token meant predicting `);` instead of “the.” Strict syntax gave Transformers strong signals, and the results were startling: autocomplete functions, suggest refactors, generate full apps. [OpenAI Codex](https://arxiv.org/abs/2107.03374) documented just how powerful this training was, forming the backbone of Copilot. [Get Codex](https://openai.com/codex/) The structure hadn’t changed. **Code is simply another language.**  *Side note:* When you write a natural-language prompt like *“write a function that multiplies two numbers”*, the text is embedded and passed through the Transformer’s hidden layers. These **latent representations** capture intent at a deeper level—mapping your words into the structures of code. By the time the model samples the next token, it’s already aligned your idea with the right programming constructs. That’s why prompting feels like programming in English. > *“To move towards AGI, models need to understand language, be able to solve math problems, and be able to code.”* --- ## Dreaming: Diffusion Transformers (DiTs) Diffusion brought a fresh challenge. Start with pure noise—like static on an old TV—and iteratively denoise until an image emerges. Traditionally, this denoising was handled by **U-Nets**, convolutional networks great at local detail. But U-Nets struggled with global structure: a face might be sharp, but the background inconsistent. By **replacing or augmenting** the U-Net backbone with attention, researchers unlocked global coherence. [Diffusion Transformers (DiTs)](https://arxiv.org/abs/2212.09748) now scale smoothly and rival—or in some cases surpass—U-Nets on benchmarks.  ### Why does it matter? Diffusion isn't just an academic trick — it's the engine behind some of the most jaw-dropping generative models today. **Image generation.** Tools like [Stable Diffusion](https://huggingface.co/stabilityai/stable-diffusion-2) and [DALL·E](https://openai.com/dall-e-2) use denoising to transform random noise into photorealistic art, design mockups, or concept sketches. **Audio and music.** Diffusion models like [AudioLDM](https://audioldm.github.io/) generate music or sound effects by refining noise into structured audio. They're also used in speech enhancement, cleaning up noisy recordings step by step. **Video.** Models like [Sora](https://openai.com/sora) extend the same principle into time. Instead of denoising a single picture, they denoise an entire sequence of frames, learning not just how each frame looks but how motion flows across them. This is why they can produce smooth, coherent clips instead of jittery frame-by-frame guesses. **Code.** Apple's recent paper on [DiffuCoder](https://huggingface.co/apple/DiffuCoder-7B-Instruct) shows how diffusion can also generate structured software. Rather than writing code left-to-right, the model starts with noisy "gibberish" programs and refines them into working implementations. The practical power of diffusion lies in its iterative refinement. By training a model to recover a signal from noise, you're giving it a way to improve outputs gradually — which translates into sharper images, smoother videos, cleaner audio, and even more reliable code. --- ## Closing thoughts And here's the truth: Transformers never stopped being Transformers. * Words became tokens. * Image patches became tokens. * Code symbols became tokens. * Even noisy latent chunks became tokens. The revolution has been one of reinterpretation. Redefine what a “token” is, and the Transformer will follow you there. That’s how we got from translation → chatbots → code generation → Stable Diffusion → DiTs. **The Transformer’s story is far from over. The only question is: what will the next token be?**