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50 Days of Building a Small Language Model from Scratch


This content originally appeared on DEV Community and was authored by Prashant Lakhera

👋 Hello, fellow AI enthusiasts!

I’m thrilled to kick off a brand-new series: 50 Days of Building a Small Language Model from Scratch. Over the next ten weeks (Mondays through Fridays at 9:00 AM PST), I’ll share my day-by-day journey of developing tiny but mighty language models, right from tokenization through deployment.

This project grew out of my experiments building two proof-of-concept models:

I learned so much by coding every component, from attention calculations to training loops, that I can’t wait to walk you through each step.

❓ Why Small Models - and Not Big Ones?

  • Accessibility. Not everyone has access to GPUs or massive compute clusters, but almost everyone has a CPU. By keeping our model under ~30 million parameters, you can train and experiment on a midrange laptop or a small cloud GPU.
  • Speed. Smaller models train faster, letting us iterate daily. Faster turnaround means more opportunities to debug, profile, and understand the impact of each change.
  • Clarity. When you build each component yourself, you see exactly how attention weights are computed, how gradients flow, and where inefficiencies hide. It’s the most effective way to learn the mechanics of Transformers and language modeling.

🤔 Why Build from Scratch When So Many Models Already Exist?
I asked myself the same question: Why reinvent the wheel? But here’s what happened when I dove in:

  • Concepts crystallized: I went from reading about self-attention and tokenization in theory to actually coding them. Suddenly, “positional encodings” and “layer norms” weren’t just buzzwords; I knew exactly where they sat in my code and how they affected training stability.
  • Debug skills leveled up: When your model output is nonsensical, you trace the error through your embedding lookup, the softmax normalization, and your optimizer’s momentum updates. You build a mental map of every moving part.
  • Tooling emerged: All those little Python scripts and config files eventually became the seed of IdeaWeaver, my all-in-one CLI for GenAI workflows. IdeaWeaver now handles dataset ingestion, training, evaluation, agents, MCP, RAG pipelines, and more with a single command.

Docs: https://ideaweaver-ai-code.github.io/ideaweaver-docs/
GitHub: https://github.com/ideaweaver-ai-code/ideaweaver

📆 Series Roadmap: What to Expect
Over the next 50 posts, here’s a high-level view of our milestones:
I’ll link every script and notebook in each post so you can clone, run, and modify along with me.

🚀 Ready to Dive In?
Mark your calendars: Monday, June 23, 2025, at 9 AM PST will be Day 1 of our series. Whether you’re brand-new to LLMs or you’ve used Transformers via high-level libraries, this series will give you a granular, code-level understanding of what makes language models tick.

Stay tuned on LinkedIn, Twitter/X /X, Medium, dev.to, and Reddit, where each post will go live, complete with code snippets, diagrams, and performance charts.

Feel free to drop questions or topic suggestions in the comments below. See you on Day 1! 🎉


This content originally appeared on DEV Community and was authored by Prashant Lakhera