percepta-ai/spotlight-vm

Spotlight VM

A small, hand-constructed Spotlight transformer that runs Python: the examples from Growing Intelligence Beyond the Weights. intelligence/ holds the weights (under 100K parameters), which never change. memory/ holds MicroPython v1.28.0 (core.safetensors) and two packages.

cc -O3 -std=c99 spotlight.c -lm -o spotlight
./spotlight < examples/euler1.py            # 233168; -v also prints every generated token
cat examples/tax_brackets/*.py | ./spotlight

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("percepta-ai/spotlight-vm", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("percepta-ai/spotlight-vm", trust_remote_code=True)
output = model.generate(**tokenizer("print(1 + 2)\n", return_tensors="pt"))
print(tokenizer.decode(output[0]))  # 3

In a local copy of this folder, from_pretrained(".") works the same way.

Example	In the post	Output
examples/euler1.py, euler2.py, euler3.py	Project Euler problems 1 to 3	233168, 4613732, 6857
examples/memory_loader.py	Loading memory selectively	4
examples/tax_brackets/	Acquiring and updating knowledge	$17400, then $16914
examples/mnist_digit.py	Growing capabilities and intelligence	7

It runs on one CPU core at about 120K tokens per second: minutes for the Project Euler examples, about half an hour for MNIST (245M tokens). This MicroPython build has no floating point. License: Apache 2.0, see LICENSE.md.