Overview
What this challenge is about.
LoRA Fine-Tune a 7B LLM for Legal-Clause Extraction. Advanced challenge in code. Writing production code that solves real engineering problems, earn a blockc...
The Brief
What you'll do, and what you'll demonstrate.
Fine-tune a 7B LLM with LoRA for legal-clause extraction and quantify whether it beats prompt-engineering on accuracy, latency, and cost per document.
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
- Implement LoRA fine-tuning on a 7B base model
- Design a fair comparison between prompted and fine-tuned baselines
- Evaluate extraction quality with span-level macro-F1
- Reason about the cost/quality trade-off for LLM products
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.
- Lora
Apply lora to solve real industry problems and demonstrate production-level capability.
- Fine Tuning
Apply fine tuning to solve real industry problems and demonstrate production-level capability.
- Parameter Efficient Tuning
Apply parameter efficient tuning 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.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Huggingface
Apply huggingface 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:
Machine Learning Engineer
Shipping a LoRA adapter end-to-end with cost analysis and a go/no-go memo is exactly the day-one work of an MLE at any LLM-powered product startup.
This challenge sharpens
- lora
- fine-tuning
- huggingface
NLP Engineer
Span-level extraction evaluation with macro-F1 across many clause types is core NLP-engineer work in any text-extraction product.
This challenge sharpens
- llm-evaluation
- fine-tuning
- huggingface
AI Engineer
Translating training results into a shippable adapter plus a cost memo for leadership is the AI-engineer craft of putting LLMs into product.
This challenge sharpens
- lora
- pytorch
- parameter-efficient-tuning