loretoparisi

loretoparisi / microgpt.c

Public

microgpt.py in C

34
6
100% credibility
Found Feb 17, 2026 at 28 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
C
AI Summary

A single dependency-free C file that trains a tiny transformer model on text data like baby names and generates similar new samples extremely quickly.

How It Works

1
🔍 Discover microgpt.c

You hear about this fun, tiny program that can learn patterns from a list of names and invent new ones, all in one simple file.

2
📥 Get ready

Download the single file and create a plain text file named input.txt with example names, one per line, like from a baby names list.

3
🚀 Start the magic

Follow the quick steps to launch it on your computer, and it begins learning super fast right away.

4
📊 Watch it learn

You see progress messages as it trains on your names in just seconds or minutes, getting smarter with each step.

✨ Enjoy new names

It generates creative new name ideas that sound just like the real ones, proving your mini AI works perfectly.

Sign up to see the full architecture

3 more

Sign Up Free

Star Growth

See how this repo grew from 28 to 34 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is microgpt.c?

microgpt.c ports Andrej Karpathy's microgpt.py to pure C, delivering a zero-dependency tool to train and run tiny GPT transformer models on text datasets. Compile with `gcc -O2 -o microgpt microgpt.c -lm`, point it at `input.txt` (one doc per line), tweak flags like `--n-embd 32 --num-steps 2000`, and it trains a model to generate samples like baby names in seconds. Unlike bloated frameworks, it handles full training, Adam optimization, and autoregressive inference in one binary.

Why is it gaining traction?

It crushes the Python version with 14,800x speedup on M1 hardware by manual gradients, fitting ~900 lines with no external libs—just libc and libm. Devs dig the embeddable speed for on-device generation and the dead-simple CLI for quick experiments. Benchmarks scale cleanly: bigger configs train in ~2s with coherent outputs.

Who should use this?

Embedded devs squeezing transformers onto MCUs without Python runtimes, ML instructors demoing causal attention on custom corpora like code or lyrics, or indie hackers training micro models for apps like name generators. Suits anyone benchmarking nano-GPTs against llama.cpp styles.

Verdict

Worth forking for toy training or education—26 stars show niche appeal—but 1.0% credibility flags immaturity; expect tweaks for real datasets. Ship with more examples to hit escape velocity.

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.