ssrhaso

ssrhaso / microjpt

Public

The most atomic way to train and run inference for a GPT in pure, 100 lines of dependency-free Julia.

gpt julia transformer
30
2
100% credibility
Found Mar 04, 2026 at 30 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Julia
AI Summary

A tiny self-running program that learns from a list of English words and generates new plausible words using basic AI techniques.

How It Works

1
👀 Discover microjpt

You find this charming project that shows how a tiny brain can learn words and dream up new ones all by itself.

2
📥 Grab the files

Download the two simple files to your computer so you can try it out.

3
Pick your style
📚
Detailed version

Go with the one full of helpful notes to understand every step.

⚡
Quick version

Pick the tiny no-frills one for instant action.

4
▶️ Start the magic

Launch your chosen file and it grabs a word list to begin learning.

5
🌟 Watch it learn

Follow the exciting progress as numbers improve, showing it's getting cleverer with each practice round.

🎉 See new words

Celebrate by reading the batch of fresh, invented words it creates, like fun new names.

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Star Growth

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AI-Generated Review

What is microjpt?

microjpt lets you train a tiny GPT from scratch and generate text like made-up English words, all in under 100 lines of pure Julia with zero dependencies. Drop it into a Julia 1.9+ REPL, and it auto-downloads a word dataset, trains for 10,000 steps via Adam optimization, then spits out inference samples at temperature-controlled creativity. Solves the pain of framework-free ML demos for folks eyeing Julia's matrix math in the github most used languages.

Why is it gaining traction?

Blasts through benchmarks as the fastest batch=1 GPT port—1,581x over CPython and 3.8x past Rust at small scale—making Julia the most atomic element for raw speed without autograd bloat. Its dependency-free purity hooks devs chasing the most github commits style minimalism, like Karpathy's original but in Julia's high-perf veins. Stands out in github most starred repos shadows by collapsing backprop to matrix ops for real-time training feedback.

Who should use this?

Julia enthusiasts benchmarking custom transformers or teaching ML from first principles. Performance hackers porting nano-models to edge devices, or educators demoing causal attention without PyTorch. Skip if you're not in the most atomic radius element of low-level numerics.

Verdict

Grab it for Julia speed experiments or GPT internals—benchmarks deliver on most atomic power country claims—but 30 stars and 1.0% credibility score flag it as early-stage with WIP docs. Solid prototype, not production-ready; fork and contribute to boost it past nanojpt's most github stars repo potential.

(198 words)

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