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🚀 Create Your Own LLM from Scratch with create-llm


This content originally appeared on DEV Community and was authored by Aniket Giri

🚀 Create Your Own LLM from Scratch with create-llm

Building a Large Language Model (LLM) doesn’t have to be complicated.

With create-llm, you can scaffold a complete LLM training pipeline in seconds — just like create-react-app, but for AI models.

✨ What is create-llm?

create-llm is an open-source CLI tool that sets up everything you need to build, train, and evaluate your own custom LLM from scratch.

It’s built for:

  • AI enthusiasts exploring LLMs
  • Researchers building domain-specific models
  • Startups needing custom AI assistants
  • Developers who want to learn the internals of training LLMs

🛠 Features

  • Full Project Scaffolding — tokenizer, dataset prep, training scripts, evaluation.
  • Custom Dataset Support — train on your own text data.
  • Synthetic Data Integration — optional integration with SynthexAI for generating high-quality synthetic datasets.
  • Choice of Tokenizers — BPE, WordPiece, Unigram.
  • Trainer-ready Pipeline — powered by PyTorch.
## 📦 Installation


npx create-llm my-llm
cd my-llm


🚂 Training Your Model

1. Prepare your dataset

python data/prepare_dataset.py --input data/raw.txt --output data/processed.txt

2. Train your tokenizer

python tokenizer/train_tokenizer.py --input data/processed.txt --output tokenizer.json --vocab-size 32000 --type bpe

3. Train your LLM

python train.py --config configs/train_config.json

🔥 Why SynthexAI?
We also built SynthexAI — a synthetic data platform that can generate millions of high-quality training samples for your model.
Instead of spending months collecting data, you can have it ready in hours.

💡 Try It Out
Run this in your terminal and start your journey into building LLMs:

npx create-llm my-llm

Let me know what you build — we’d love to feature cool projects on SynthexAI.


This content originally appeared on DEV Community and was authored by Aniket Giri