ML Without Magic: Building a Tiny Language Model in Pure Node.js and Watching Every Weight Change

Tokenization → embeddings → causal Transformer → LM head → softmax → loss → backpropagation. No TensorFlow, no PyTorch, and no hidden autograd. Repository: tiny-language-model-neuro-js . Most explanations of language models present correct formulas but hide the path between them inside a framework. I wanted the opposite: one small scenario where every scalar is visible and where the terminal clearly shows incorrect answers before learning and correct answers after it. The project now has one command: node src/train.js --generalize --adaptive-teach It requires Node.js 18.19+ and has no dependencies. The result first The model is queried immediately after random initialization: BEFORE TRAINING — random, usually wrong answers > can human read ? model: ? <unk> ... expected: human can read. [WRONG] > can fish swim ? model: ? <unk> ... expected: fish can swim. [WRONG] > can cat read ? model: ? <unk> ... expected: cat cannot read. [WRONG] After pre-training, SFT, and adaptive SFT, the same model produces: FINAL ANSWERS AFTER ADAPTIVE SFT > can human read ? model: human can read. [CORRECT] > can fish swim ? model: fish can swim. [CORRECT] > can bird fly ? model: bird can fly. [CORRECT] > can cat read ? model: cat cannot read. [CORRECT] Rehearsal controls preserved: 14/14. Stable criterion reached 11 times in a row. The initial text varies because initialization is random. The final acceptance criterion does not: all answers must be correct, every target token must have at least 95% probability, and the complete check must pass more than ten times consecutively. What remains after removing the extra modes The code previously contained several debug and training modes. They were useful while experimenting but obscured the main idea. The final version keeps one educational pipeline: text → word tokenization → token IDs → token + position embeddings → two causal Transformer blocks → multi-head self-attention → two-hidden-layer FFN → LM head → softmax → next-token probabilities