How to Build Your Own Tiny LLM From Scratch

2026-07-08 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

This guide outlines a five-stage pipeline for building Large Language Models (LLMs), demystifying the process behind systems like GPT and Claude. It begins with Data & Tokenization, where raw text is cleaned and converted into integer IDs, explaining why models "see" tokens, not words. Next is Pretraining, focusing on next-token prediction, which builds a base model capable of plausible text completion but prone to hallucination. Supervised Fine-Tuning (SFT) then teaches the model to follow instructions, shaping its "personality" with curated examples. The fourth stage, Preference Modeling, involves collecting human preference data to train a Reward Model or use Direct Preference Optimization (DPO) for automated evaluation. Finally, Alignment Optimization refines the model's behavior for nuance and safety, though it may incur an "Alignment Tax" on raw capabilities. The article provides practical tools like `tiktoken`, `nanoGPT`, `Unsloth`, and Hugging Face TRL for each stage.

Key takeaway

For AI engineers or ML students aiming to build or deeply understand LLMs, this pipeline clarifies how models evolve from raw text to helpful assistants. You should apply this staged understanding to diagnose model behaviors like hallucination, refine your prompting strategies by considering the statistical context, and leverage tools like `nanoGPT` or `Unsloth` to build your own small, functional LLM, demystifying the underlying mechanics.

Key insights

LLMs are constructed via a five-stage pipeline: data processing, pretraining, supervised fine-tuning, preference modeling, and alignment optimization.

Principles

• LLMs process tokens, not words; pretraining prioritizes plausibility, causing hallucinations.
• Supervised Fine-Tuning (SFT) instills instruction-following and "taste."
• Alignment can incur a "tax" on raw model capabilities.

Method

Build an LLM by sequentially applying five stages: data preparation, next-token prediction pretraining, supervised instruction fine-tuning, human preference modeling, and final alignment optimization for behavior and safety.

In practice

• Process data with `tiktoken` or Hugging Face tokenizers; train tiny models via `nanoGPT`.
• Fine-tune Llama 3 8B with `Unsloth` or `Axolotl`.
• Apply DPO using Hugging Face TRL for feedback.

Topics

• LLM Pipeline
• Tokenization
• Pretraining
• Supervised Fine-Tuning
• Alignment Optimization
• Direct Preference Optimization

Code references

• openai/tiktoken
• karpathy/nanoGPT
• unslothai/unsloth
• axolotl-ai-cloud/axolotl

Best for: AI Engineer, Machine Learning Engineer, AI Student

Related on AIssential

• Building LLM From Scratch: Understanding How Large Language Models Work — Large Language Models fundamentally predict the next token by processing numerical representations of text through attention mechanisms.
• Expert Masking for More Efficient Expert Offloading — Fine-tuning can degrade existing factual knowledge, but strategies like parameter restriction or self-distillation can mitigate this.
• Pokémon Built a Robot Brain — LLMs are trained in stages, from broad knowledge acquisition to fine-tuned conversational and reasoning capabilities.
• Developing an LLM: Building, Training, Finetuning — LLM development involves building architecture, pre-training on vast datasets, and fine-tuning for specific tasks and behaviors.
• The Mind Behind the Machine: A Deep Look at How Large Language Models Actually Work — Transformers utilize attention for context-aware representations, enabling LLMs to learn language statistics and scale for emergent abilities.
• Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs — Training an LLM from scratch involves understanding core components like tokenization, transformer architecture, and the training loop.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.