FineTune Helper

Local helper app for creating assignment-level fine-tuning records.

Start

Copy .env.example to .env
Fill in the hosted AI endpoint, key, and model
Fill in the backend generator URL and teacher token
Run from repo root:

make fine-tune

Then open:

http://localhost:4310

What it does now

generates a full assignment from the real backend POST /api/questions/generate endpoint using:
- topic
- difficulty
- count
stores the assignment in the same shape the real review flow expects:
- assignment metadata
- question list
- student submission per question
- teacher review per question
- assignment summary
- recommended next step
can ask the hosted model to draft:
- the full student submission for all questions
- the full teacher review package for all questions plus assignment summary
shows:
- a canonical saved record preview
- a chat-style fine-tune JSON preview
saves reviewed examples locally in your browser
lets you load, update, and delete saved examples
exports either:
- dataset.jsonl
- train.jsonl + val.jsonl

Saved record shape

The helper now targets one saved row per assignment:

assignment-review-v1
  assignment
  studentSubmission
  teacherReview.questions[]
  teacherReview.assignmentSummary
  teacherReview.recommendedNextStep

This matches the real app's mixed-granularity review flow:

one assignment review call
  -> question-level labels for every question
  -> one assignment-level summary

Backend generator auth

Set:

FINE_TUNE_BACKEND_URL to your BoostAI base URL, for example https://boost.ai.moku.build
FINE_TUNE_BACKEND_TOKEN to a valid teacher JWT/session token value

The helper forwards that token as:

Authorization: Bearer <token>

so it can call the protected backend generator endpoint from the separate local helper app.