Aniket Giri

Posted on Oct 24

Train Your First LLM in 5 Minutes: A Complete Beginner's Guide

#python #ai #machinelearning

Train Your Own Language Model in Under 5 Minutes

Ever wondered how ChatGPT or Claude are trained? You can train your own language model in under 5 minutes. Here's how.

Why Train Your Own LLM?

Before we dive in, you might ask: "Why bother training my own when ChatGPT exists?"

Fair question. Here's why:

Understanding: You learn how LLMs actually work, not just how to use them
Privacy: Your data stays local, perfect for sensitive information
Customization: Train on your specific domain (legal docs, medical data, code)
Cost: No API fees for inference once trained
Learning: Best way to understand AI is to build it

Plus, it's genuinely fun to chat with a model you trained yourself.

What We're Building

By the end of this tutorial, you'll have:

✅ A trained language model (681K parameters)

✅ Understanding of tokenizers, training, and generation

✅ A working chatbot you can talk to

✅ Foundation to train larger models

Total time: ~5 minutes

Prerequisites

You'll need:

Node.js (18+): Download here
Python (3.8+): Probably already installed
5 minutes: Seriously, that's it
GPU (optional): Works on CPU, faster with GPU

That's all. No ML background needed.

Step 1: Create Your Project (30 seconds)

Open your terminal and run:

npx create-llm my-first-llm --template nano
cd my-first-llm

This scaffolds a complete LLM training project. Think of it like create-next-app but for language models.

What just happened?

Created project structure
Set up training scripts
Added sample data
Configured everything with smart defaults

Step 2: Install Dependencies (1 minute)

pip install -r requirements.txt

This installs PyTorch, transformers, and other ML libraries. Grab a coffee while it runs.

Step 3: Train a Tokenizer (30 seconds)

python tokenizer/train.py --data data/raw/sample.txt

Output:

Training BPE tokenizer...
Vocabulary size: 422
✓ Tokenizer saved to: tokenizer/tokenizer.json

What's a tokenizer?

It breaks text into pieces the model can understand.

Example:

Input: "Hello world"
Tokens: ["hello", "world"]
Token IDs: [156, 289]

The model learns from these numbers, not raw text.

Step 4: Prepare Your Data (15 seconds)

python data/prepare.py

Output:

Created 9,414 examples
Training tokens: 4,819,968
✓ Data preparation complete!

This processes your text into training examples with the right format.

Step 5: Train the Model (90 seconds)

Here's where the magic happens:

python training/train.py

You'll see:

Step 100: Loss 1.09, Tokens/s: 43,628
Step 200: Loss 0.10, Tokens/s: 38,536
Step 500: Loss 0.03, Tokens/s: 33,161
Step 1000: Loss 0.01, Tokens/s: 32,555

✅ Training completed!

What's happening?

The model is learning patterns in your text
Loss going down = model getting better
1000 training steps in ~90 seconds
Creates checkpoints as it trains

Side note: The nano template is intentionally small (681K params) so it trains in 1-2 minutes on any laptop. It will likely show mode collapse (repeating words) - that's expected and educational! Upgrade to --template tiny for better results.

Step 6: Chat With Your Model! (30 seconds)

Time to see what you built:

python chat.py --checkpoint checkpoints/checkpoint-best.pt

Try it:

You: Hello
Assistant: [generates text]

You: Once upon a time
Assistant: [generates story]

What to expect with nano:

The model might repeat words or show mode collapse:

You: Once upon a time
Assistant: time time time time time...

This is normal! The nano template is designed to be fast and educational. It shows you what happens with small models and limited data.

For better quality, use the tiny template:

npx create-llm my-better-llm --template tiny
# Trains in 5-10 minutes, much better results

Understanding What Just Happened

The Model

681,856 parameters (nano template)
3 transformer layers
Trained on Shakespeare (sample data)
Vocab of 422 tokens

This is tiny compared to GPT-4 (175 billion params), but it's enough to learn basic patterns!

The Training

1000 steps in 90 seconds
Perplexity: ~1.01 (very low = overfitting)
Learning rate: 5e-4 with warmup
Batch size: 8

The model memorized the training data (overfitting) because it's small. That's okay for learning!

Going Further

1. Use More Training Data

The sample includes ~5MB of text. For better results, add more:

# Download more books
curl https://www.gutenberg.org/files/11/11-0.txt > data/raw/alice.txt
curl https://www.gutenberg.org/files/1342/1342-0.txt > data/raw/pride.txt

# Retrain
python data/prepare.py
python training/train.py

2. Try a Bigger Model

npx create-llm my-tiny-llm --template tiny
cd my-tiny-llm
# ... same steps, but 2-5M parameters

Templates:

nano: 681K params, 1-2 min, learning
tiny: 2-5M params, 5-10 min, usable
small: 50-100M params, 1-3 hours, production
base: 500M-1B params, 1-3 days, research

3. Deploy Your Model

python deploy.py --checkpoint checkpoints/checkpoint-best.pt --to huggingface

Share your model with the world!

4. Fine-tune on Your Data

# Add your own text files to data/raw/
cp ~/my-documents/*.txt data/raw/

# Retrain
python data/prepare.py
python training/train.py

Train on customer support conversations, code, legal docs, anything!

Common Issues & Solutions

"Perplexity too low!"

⚠️  WARNING: Perplexity < 1.1 indicates severe overfitting!

Solution:

Add more training data
Use smaller model
Increase dropout in llm.config.js

This warning is a feature - it teaches you about overfitting!

"Out of memory"

# Edit llm.config.js
training: {
  batch_size: 4,  // Reduce from 8
}

"Model repeating words"

This is mode collapse - the model learned limited patterns.

Solutions:

Use --template tiny instead of nano
Add more diverse training data
Train longer (increase max_steps)

"Training takes forever"

Use a GPU if possible
Reduce max_steps in config
Use smaller template (nano is fastest)

What You Learned

In 5 minutes, you:

✅ Trained a neural network with 681K parameters

✅ Understood tokenization (text → numbers)

✅ Ran training loop (loss optimization)

✅ Generated text (inference)

✅ Saw overfitting (perplexity warnings)

This is more ML knowledge than most bootcamps teach in weeks!

Next Steps

Learn More

Read the full documentation
Join our Discord community
Check out example projects

Build Something

Ideas for your next model:

Code completion (train on GitHub repos)
Writing assistant (train on your writing style)
Domain expert (train on technical docs)
Creative writer (train on novels)

Share Your Results

Built something cool? Share it!

Tag #createllm on Twitter
Post in our Discord
Submit to our showcase

The Bigger Picture

This is just the beginning.

create-llm makes local LLM training accessible, but the future is cloud training platforms, model marketplaces, and one-click deployments.

Think: Vercel for LLMs.

Want to be part of that future? Star the project, join the community, and let's build it together.

Try It Now

npx create-llm my-first-llm

5 minutes from now, you'll have trained your own LLM.

Not perfect. Not production-ready. But yours.

And that's how you learn.

About the Project

create-llm is open source and built by developers frustrated with complex ML tutorials.

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