A language model dismantled into pieces small enough to understand, train, and rebuild.
next-char-model is a pedagogical project that progressively builds a small character prediction model, moving from embeddings toward multi-head attention and transformer-style blocks.
The problem
Language models quickly become opaque when you try to understand them only in their final form. To really learn how they work, it is often more useful to decompose each mechanism and add it one step at a time.
Design & implementation
next-char-model follows that exact logic. The project is organized as numbered scripts that progressively introduce the parts of a small autoregressive model: tokenization, embeddings, positions, attention, projections, residual connections, MLP layers, layer normalization, and then multi-head attention.
This makes the project a very readable learning path. It is not trying to become a state-of-the-art model. It is trying to create a concrete progression across concepts that often stay abstract.
The project also includes embedding visualizations and a saved checkpoint, making it possible to move from learning code to a small loop of generation and inspection.
What it does
- Pedagogical PyTorch character-level language model
- Explicit progression from embeddings to blocks with multi-head attention
- Embedding visualizations before and after training
- Loading and reuse of a saved checkpoint
Why I built it
next-char-model makes language-modeling mechanisms easier to manipulate. Its value is less in producing a large model than in making the logic that leads to a simplified transformer understandable.
