LLM Training Loop Simulator
Simulate training an LLM across multiple epochs: batches, forward passes, cross-entropy loss, and AdamW weight updates with live loss curves.
Concept Breakdown: How Neural Networks Train & Backpropagate
Imagine throwing a dart blindfolded. A coach shouts: "You were 2 feet too far left!" (Loss). You nudge your arm 2 inches right (Backpropagation). After 10,000 throws, you hit the bullseye every time!
When a student memorizes practice test answers word-for-word instead of learning the concept. They score 100% on the practice test, but fail the real exam (validation loss shoots up).
Click "Start Training Run" below. Watch the loss drop from 11.2 (pure random noise) down to 1.8 (fluent language understanding).
Model Parameters & GPU VRAM Calculator
Quick Reference & Instructions
Simple steps, pro tips, and execution details
Provide Inputs
Type, paste, or select your values in the form fields below.
Instant Live Analysis
Calculations and formatting happen automatically with zero delay as you type.
Copy or Use Output
Copy results or apply the clean output directly to your projects.
How It Works
Runs an animated client-side training loop showing training vs validation loss convergence and parameter drift.
Frequently Asked Questions
Common questions about calculations, assumptions, and edge cases.
When training loss keeps falling but validation loss starts rising, the model is beginning to memorize the training data rather than generalize — a pattern known as overfitting.
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Build a Mini Language Model in Browser
Train an educational language model on your own text corpus. Inspect vocabulary, forward predictions, training loss, and generate continuations.