Tokenizer

"A clear breakdown of Transformer layers—LayerNorm, Attention, FeedForward, and Residuals—explained step-by-step with visuals."

When a leading AI scientist like Yann LeCun says, "AI systems will have emotions," it sounds like science fiction — or a warning. But the truth is far more complicated, and far more important to understand.

Learn how to safely extend Hugging Face tokenizers with domain-specific vocabulary, resize model embeddings, and preserve compatibility for fine-tuning without retraining from scratch.

“No matter how advanced your model or pipeline, it’s only as good as the truth it learns from. Ground truth isn’t just the start — it’s the standard that guides the entire machine learning journey.”

“The Discriminator knows the domain. The Generator starts with nothing. It generates gibberish, gets rejected, and slowly adapts — until it produces something so good, the Discriminator can't tell anymore.”

We built a BERT-powered router to classify query complexity and smartly route to LLMs like GPT-4 or Mistral—balancing cost, speed, and accuracy with ONNX quantization.

The Belief State Transformer (BST) enhances AI text generation by encoding both past and future context, ensuring coherence in long-form content. Unlike traditional models that predict words based only on past tokens, BST constructs a global belief state using bidirectional reasoning.

Distillation in the context of machine learning, particularly as used by companies like DeepSeek or others working with large-scale