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MiniMind

TOOL

Train a 64M-parameter LLM from scratch in just 2 hours for $3. Complete from-scratch implementation covering MoE, data cleaning, pretraining, SFT, LoRA, RLHF (DPO/PPO/GRPO), tool use, and model distillation. All core algorithms implemented in pure PyTorch without high-level abstractions. Educational framework for understanding LLM internals. Apache 2.0 licensed

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Overview

MiniMind: a free, copy-ready tool on OpenRuna. Train a 64M-parameter LLM from scratch in just 2 hours for $3. Complete from-scratch implementation covering MoE, data cleaning, pretraining, SFT, LoRA, RLHF (DPO/PPO/GRP

What this tool does

"MiniMind" is a tool you can copy, adapt, and run with any modern AI assistant. Train a 64M-parameter LLM from scratch in just 2 hours for $3. Complete from-scratch implementation covering MoE, data cleaning, pretraining, SFT, LoRA, RLHF (DPO/PPO/GRPO), tool use, and model distillation. All core algorithms implemented in pure PyTorch without high-level abstractions. Educational framework for understanding LLM internals. Apache 2.0 licensed OpenRuna cross-links it to related prompts, agents, and tools, which makes assembling a full workflow around it straightforward. Paste it straight into a chat, drop it into a system prompt, or store it as a reusable skill.

Use cases

  • Keep it in a shared library as the canonical version of this tool for your organisation.
  • Fork it as a baseline and layer in your own project context, constraints, and examples.
  • Hand "MiniMind" to a new teammate so their tool output matches your team's quality bar from day one.
  • Use "MiniMind" when you need a repeatable tool for professional work without rewriting instructions every time.

Example output

Ask the model to apply "MiniMind" to your scenario and it returns a structured answer — clear sections, actionable steps, and assumptions stated upfront — ready to paste into docs, tickets, or code comments. Expect 2–3 iterations to dial in tone and depth for your context.

Tips by platform

Claude

Claude works best when you paste this tool up front and ask it to outline its plan before writing. Use the artifact panel to refine structured output turn by turn.

ChatGPT

ChatGPT responds well when you paste this tool and immediately give one concrete example of your input. Use a reasoning-capable model for multi-step work.

Cursor

Add this tool to your Cursor rules and invoke it from Agent mode for repeatable results. Link back to its OpenRuna page in the rule so the source stays discoverable.

Frequently asked questions

What is "MiniMind"?
It is a tool listed on OpenRuna — Train a 64M-parameter LLM from scratch in just 2 hours for $3. Complete from-scratch implementation covering MoE, data cleaning, pretraining, SFT, LoRA, RLHF (DPO/PPO/GRPO), tool use, and model distillation. All core algorithms implemented in pure PyTorch without high-level abstractions. Educational framework for understanding LLM internals. Apache 2.0 licensed You can copy and adapt it for ChatGPT, Claude, Cursor, or any other AI assistant.
Is "MiniMind" free to use?
Most OpenRuna resources are open or CC0-licensed. Check the license shown on this page before commercial use; premium collections are clearly marked as such.
How do I get the best results from this tool?
Replace any placeholders, add your project context, and ask the model to confirm its assumptions first. Iterate over 2–3 follow-up turns rather than expecting a perfect first response.
Does "MiniMind" work with both Claude and ChatGPT?
Yes — it is model-agnostic text, so it runs on Claude, ChatGPT, Gemini, and Cursor. The tips on this page cover each of those assistants specifically.
Where can I find resources related to "MiniMind"?
Scroll to the Related resources section on this page, or open the matching category hub on OpenRuna to find connected prompts, tools, agents, and datasets in the same topic area.

Related resources