mote

A tiny language model, run from scratch in C.

mote is a complete transformer inference engine in a few hundred lines of dependency-free C. No PyTorch, no NumPy, no BLAS, no framework. It loads a pretrained model and generates text, and the whole forward pass, attention, rotary embeddings, the KV cache, SwiGLU, sampling, is written out by hand so you can read every step.

The name is the point. A mote is the smallest thing you can still see. The goal of this project is to shrink a working language model down until it fits somewhere absurd: a laptop with nothing installed, and eventually a microcontroller.

$ ./mote models/stories15M.bin -i "Once upon a time, there was a little robot"

Once upon a time, there was a little robot. The robot liked to crawl. One day,
the robot went to the park. He saw a big tree... At the top, he saw a big party.
The party was for the robot. The robot and the bird were friends. They played
and had fun.

[158 tokens, 109.4 tok/s]

Quickstart

make                              # builds ./mote, needs only a C compiler
./scripts/download_model.sh       # fetches a 15M-param model + tokenizer (~58MB)
./mote models/stories15M.bin -i "Once upon a time"

Or run it quantized, 4x smaller (int8) or 6x smaller (int4) and noticeably faster:

make quantize                                        # builds the converter
./quantize models/stories15M.bin models/stories15M.mq
./mote models/stories15M.mq -i "Once upon a time"    # int8, same output quality
./quantize models/stories15M.bin models/stories15M_q4.mq 32 4
./mote models/stories15M_q4.mq -i "Once upon a time" # int4, still coherent

Options:

-z <path>   tokenizer file (default models/tokenizer.bin)
-t <float>  temperature, 0 = greedy/deterministic (default 1.0)
-p <float>  top-p / nucleus sampling (default 0.9, 0 disables)
-n <int>    number of steps (default 256)
-i <string> prompt
-s <int>    rng seed, for reproducible runs

Larger models (42M, 110M) are one argument away: ./scripts/download_model.sh 110M.

How it works

The engine is small enough to hold in your head:

file	what it does
src/model.c	memory-maps a checkpoint and points the weights at it
src/forward.c	one token through the network, the core of the whole thing
src/tokenizer.c	byte-pair encode/decode between text and token ids
src/sampler.c	greedy, temperature, and top-p sampling
src/quant.c	int8 and int4 quantization: the pack, dequant, and integer matmuls
src/mote.c	the CLI and the generation loop
tools/quantize.c	converts an fp32 checkpoint to the quantized .mq format

The model is a standard Llama-style decoder: pre-norm blocks, each with grouped-query attention and a SwiGLU feed-forward, RMSNorm throughout, and rotary position embeddings applied directly to the query and key vectors. A KV cache keeps each step to attending over history rather than recomputing it. Weights are memory-mapped, so a model is paged in on demand and startup is instant.

There is exactly one performance-critical loop, the matrix-vector matmul, and it is the only thing touched by optional multithreading:

make omp      # OpenMP build, if your compiler has it

Quantization

mote also runs quantized weights, at two widths. int8 packs each big weight matrix into 8-bit integers with one fp32 scale per group of values (symmetric "Q8_0" quantization). int4 goes further: two weights per byte in [-8, 7], where the scale carries the sign of the group's largest weight so that weight lands exactly on -8 and every other weight gets a finer step (the "Q4_0" idea). In both, the runtime quantizes each activation to int8 on the fly and the matmul is integer work with the scales folded back in per group. The norm gains stay fp32.

One binary runs all of it: a checkpoint's first bytes say whether it is fp32 or .mq, and the header says how wide the weights are. And the quality is measured, not asserted: make perplexity scores a model over held-out text (TinyStories validation data, ~17k tokens). On the 15M model:

weights	size	perplexity	cost
fp32	61 MB	2.376	—
int8 (gs 32)	17 MB	2.379	+0.1%
int4 (gs 32)	9.5 MB	2.640	+11%

Getting the int4 number down was an exercise in measuring rather than trusting intuition: a per-group clipped-scale search lowers weight-space RMS error and worsens perplexity, because the weights it clips are precisely the ones that matter. The signed-scale trick above beat every clipping scheme tried; the small scale search kept in the converter is worth ~0.1% on top. Weight RMS error and model quality are different objectives, only one of them is the point.

Because quantized weights are a quarter (or an eighth) of the size, quantized models are also markedly faster to run, memory bandwidth is the bottleneck, not arithmetic.

In a browser

The engine also compiles to WebAssembly — same C files, one more host:

./web/build.sh                    # needs emscripten; emits web/mote.js + .wasm
python3 web/serve.py 8000         # http.server + the two isolation headers
# open http://localhost:8000/web/  (append ?auto to run a benchmark prompt)

The whole engine is ~87KB of wasm; the model download is the only heavy part. The matmuls use hand-written wasm SIMD and a pthread worker pool (pthreads are web workers; they need the cross-origin-isolation headers serve.py sends — without them the engine still runs, single-threaded). Measured with the int4 Qwen2.5-0.5B (309 MB): 48 tok/s in desktop Chromium and 20 tok/s in iPhone Safari, up from 13.6 and 6.5 single-threaded. Still shy of the native app, but a real conversation from a URL with nothing installed.

Roadmap

mote is being built in three milestones, from the general to the absurd. See ROADMAP.md.

1. Inference in pure C — done. This is what you are running now.
2. Quantize the weights, with the math by hand — done at both widths: int8 (4x smaller, quality intact) and int4 (6x smaller, quality measured above).
3. Onto a microcontroller — run it on a chip, not a computer.

Acknowledgements

The pretrained weights are the TinyStories models trained by Andrej Karpathy, and mote reads the compact checkpoint format from his llama2.c project. That format and those models are what make a from-scratch engine testable on day one. The transformer architecture is Meta AI's Llama. The code here is my own implementation.

License

MIT. See LICENSE.