gakonst

gakonst / mpp-nanogpt-modal

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Train nanoGPT on Modal GPUs, paid with stablecoins via MPP. No API keys. No signup. Just HTTP 402.

14
3
100% credibility
Found Mar 26, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This repository demonstrates training a small language model on Shakespeare text using cloud GPUs paid automatically with stablecoins, requiring no accounts or signups.

How It Works

1
🔍 Discover the magic

You hear about a fun way to teach a computer to write Shakespeare stories using cloud power, paid with digital money—no signups needed.

2
📥 Grab the starter kit

Download and set up a simple helper app on your computer to handle everything.

3
💳 Connect your wallet

Link your digital money wallet so payments happen automatically and securely.

4
🚀 Start the adventure

Run the easy command, and it instantly rents a powerful computer in the cloud, pays with your digital coins, and begins training the storyteller.

5
📈 Watch it learn

Follow along as the AI practices writing, getting better with each step, shown right on your screen.

🎭 Enjoy Shakespeare magic

Celebrate with funny, Shakespeare-like stories generated by your trained AI, all done in minutes without hassle.

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AI-Generated Review

What is mpp-nanogpt-modal?

This Python project lets you train nanoGPT models on Modal GPUs, paid instantly with stablecoins via MPP—no API keys, no signup, just HTTP 402 payments. Fire up `python3 run.py` after installing the Tempo CLI, and it spins a cloud T4 GPU, clones nanoGPT, trains a tiny GPT on Shakespeare for 500 iterations, then spits out loss metrics and generated text in under 100 seconds. It's a frictionless github train ai model demo that handles git clone, data prep, training, and cleanup over simple HTTP.

Why is it gaining traction?

The killer hook is agent-ready compute: pay per GPU session with USDC, no accounts blocking scripts or bots, unlike clunky cloud consoles. Developers dig the quick experiments—tweak LR or dropout via CLI flags, batch sweeps across A10G GPUs, or run full hyperparam hunts on H100s—all streaming live logs without vendor lock-in. It stands out for github train llm from scratch or train github copilot on your own data, blending Modal's serverless GPUs with MPP's on-chain receipts.

Who should use this?

AI tinkerers sweeping hyperparameters on nanoGPT before scaling to bigger runs. Indie devs training LoRA adapters or small LLMs without juggling AWS credits. Crypto AI builders scripting autonomous agents to rent GPUs for train model jobs, like simulating github train simulator fleets or buying github train ticket-style compute on demand.

Verdict

Grab it for proof-of-concept training if you're into MPP's no-keys future—docs are crisp, quickstart works out of the box. But with 14 stars and 1.0% credibility score, it's raw and experimental; expect tweaks for production-scale github train ai.

(198 words)

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