Overview
What this challenge is about.
Instruction-Tune a Small Model for an Edtech Tutor. Advanced challenge in code. Writing production code that solves real engineering problems, earn a blockch...
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.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
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