Overview
What this challenge is about.
Instruction-tune a 1.5B model on 20,000 math-tutoring dialogues and write a dataset playbook to earn a verifiable certificate.
The scenario
The edtech startup (around 35 people, 90,000 monthly active K-12 students across India and the UAE) targets shipping the tuned model on mid-range Android tablets where API-call latency is unacceptable to families on slow mobile data.
The Brief
What you'll do, and what you'll demonstrate.
Instruction-tune a 1.5B model into a pedagogy-aware math tutor under tight compute and produce the dataset-curation playbook for ongoing iteration.
Earning criteria — what you'll demonstrate
- Curate an instruction dataset with explicit quality controls
- Run supervised fine-tuning on a small open model
- Evaluate LLM outputs against both accuracy and pedagogy rubrics
- Document a dataset-curation process the next team member can follow
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Instruction Tuning
Apply instruction tuning to solve real industry problems and demonstrate production-level capability.
- Supervised Fine Tuning
Apply supervised fine tuning to solve real industry problems and demonstrate production-level capability.
- Dataset Curation
Apply dataset curation to solve real industry problems and demonstrate production-level capability.
- Llm Evaluation
Apply llm evaluation to solve real industry problems and demonstrate production-level capability.
- Huggingface
Apply huggingface to solve real industry problems and demonstrate production-level capability.
- Synthetic Data
Apply synthetic data 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:
NLP Engineer
Curating instruction data and supervised fine-tuning a small open model is exactly the day-one work of an NLP engineer at any product team shipping their own LLM.
This challenge sharpens
- instruction-tuning
- dataset-curation
- supervised-fine-tuning
AI Engineer
Shipping a tuned on-device-ready model with pedagogy rubrics is core AI-engineer work at any consumer LLM product.
This challenge sharpens
- instruction-tuning
- llm-evaluation
- huggingface
Machine Learning Engineer
Documenting a dataset-curation pipeline for ongoing iteration is the MLE craft of building processes that survive team turnover.
This challenge sharpens
- dataset-curation
- synthetic-data
- huggingface