Build a Small Transformer from Scratch and Train It on Code
Overview
What this challenge is about.
Build a small transformer from scratch, train it on code, and report perplexity. Earn a verifiable certificate.
The scenario
The lab (anon, around 200 researchers) screens new researchers on whether they can ship from-scratch transformer code, not just call .from_pretrained().
The Brief
What you'll do, and what you'll demonstrate.
Implement and train a 30M-parameter decoder-only transformer from scratch on a code corpus with proven attention + training understanding.
Earning criteria — what you'll demonstrate
- Implement self-attention, RoPE, and an LM head from first principles
- Train a transformer end-to-end on a non-toy corpus
- Visualize and interpret attention patterns
- Diagnose and fix training instabilities (loss spikes, NaNs)
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Neural Networks for NLP
Master · Nlp
Strong alignment
This challenge maps to Neural Networks for NLP at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Transformers
Apply transformers to solve real industry problems and demonstrate production-level capability.
- Self Attention
Apply self attention to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Language Modeling
Apply language modeling to solve real industry problems and demonstrate production-level capability.
- Training Debugging
Apply training debugging to solve real industry problems and demonstrate production-level capability.
- Rope
Apply rope to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
ML Researcher
From-scratch transformer implementation is the canonical research-team initiation; this challenge gives the student exactly that portfolio piece.
This challenge sharpens
- transformers
- self-attention
- language-modeling
Research Scientist
Implementing and ablating positional encodings is the kind of foundational work that research scientists do daily on architecture-research teams.
This challenge sharpens
- self-attention
- rope
- training-debugging
Applied AI Scientist
Deep PyTorch fluency at the layer-implementation level translates directly into applied AI work where standard frameworks aren't enough.
This challenge sharpens
- pytorch
- transformers
- training-debugging