Entrpi

Entrpi / eemicrogpt

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The most extreme way to train a GPT in pure, dependency-free C. 19000x faster than Python. Optimized for Apple Silicon with SME2.

11
0
100% credibility
Found Mar 04, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
C
AI Summary

A standalone program that rapidly trains a small model on a dataset of names to generate new ones, designed for peak speed on Apple Silicon Macs.

How It Works

1
📰 Discover EEmicroGPT

You stumble upon a fun project that lets you train a tiny name generator incredibly fast right on your Apple Mac.

2
📥 Grab the files

Download one simple program file and a list of real names to use as examples.

3
⚙️ Set it up quickly

Place the files together and prepare the program with a single easy step on your Mac – no extras needed.

4
▶️ Start training

Launch it and watch as it learns patterns from the names list in seconds or minutes, getting smarter step by step.

5
📈 Check progress

See the loss numbers drop, showing it's mastering how names work without any hassle.

✨ Generate cool names

Ask it to create new names and smile at the realistic, creative results it spits out instantly.

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Star Growth

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AI-Generated Review

What is eemicrogpt?

eemicrogpt trains a tiny 1-layer GPT from scratch—forward pass, backward pass, Adam optimizer, and autoregressive generation—on Karpathy's names dataset, all in a single dependency-free C file. Compile with clang on Apple Silicon for up to 19,000x speedup over Python equivalents, hitting low bits-per-character loss in seconds. Developers get instant training runs and name generation via simple command-line execution, configurable at compile time for model size or steps.

Why is it gaining traction?

It crushes Python, Rust, and C++ autograd ports by 44x on equivalent workloads, while beating MLX on Apple's GPU for seconds-scale sweeps—ideal for hyperparameter hunts without framework overhead. The single-file purity and SME2 optimizations deliver L1-cache speedups that make GPU "killer microseconds" irrelevant for micro-models. Devs dig the raw benchmark tables showing one P-core rivaling $40K hardware.

Who should use this?

Apple Silicon ML tinkerers prototyping character-level models or teaching neural net basics without PyTorch setup. Embedded devs needing offline toy LLMs for games or generators. Performance chasers benchmarking "from-scratch" training on laptops.

Verdict

Grab it for ultra-fast experiments on small GPTs—impressive engineering despite 11 stars and 1.0% credibility score signaling early maturity. Lacks tests or broad docs, but README benchmarks make evaluation dead simple.

(198 words)

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