In the original Transformer (Vaswani et al., 2017), each input token is represented as a 512-dimensional vector. This isn’t arbitrary — it’s a design choice based on model capacity and performance:
✅ Reasons for 512D:
- Representation Power:
Higher dimensions can represent more complex patterns, relationships, and nuances in the data. For language, where tokens have subtle and deep contextual meanings, 512D helps capture these. - Multi-Head Attention:
In the original Transformer, 512D is split across 8 attention heads, each with 64D. So:512 = 8 heads × 64 dimensions/head. - Depth of Encoding:
Lower-dimensional embeddings like 10D might lose important information or compress too much, leading to poor model performance on complex tasks. - Empirical Results:
Through experimentation, researchers found that models perform better with higher-dimensional embeddings — not just for language, but for images, code, and audio as well.
🤔 Then why not just use 10D?
You can, but with limitations:
- It works only for very simple tasks or toy datasets.
- The model will struggle to learn rich patterns.
- Think of it like compressing HD video into a tiny 10px image — you lose too much detail.
🔍 Example:
If you’re training a mini-Transformer for an educational demo or a small dataset, you might get away with 10D or 32D. But for real-world LLMs like BERT, GPT, etc., high dimensions like 512, 768, 1024, or 2048 are necessary.
TL;DR
You can try using 10D, but 512D is chosen to:
- Boost representational capacity
- Match attention head design
- Preserve semantic richness
- Improve learning in complex tasks