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llm.c is a minimalist, systems-level implementation of a small transformer-based language model in C that prioritizes clarity and educational value. By stripping away heavy frameworks, it exposes the core math and memory flows of embeddings, attention, and feed-forward layers. The code illustrates how to wire forward passes, losses, and simple training or inference loops with direct control over arrays and buffers. Its compact design makes it easy to trace execution, profile hotspots, and understand the cost of each operation. Portability is a goal: it aims to compile with common toolchains and run on modest hardware for small experiments. Rather than delivering a production-grade stack, it serves as a reference and learning scaffold for people who want to “see the metal” behind LLMs.
Features
- Compact C implementation that demystifies transformer internals
- Embedding, attention, and MLP layers written in a readable style
- Simple training and sampling loops to experiment end to end
- Minimal dependencies and portable build targets
- Transparent memory layout for profiling and optimization practice
- Command-line usage to run tiny demos and inspect outputs
Project Samples
