# FINETUNE.md — `midnight-static-writer` LoRA

Goal: Nemotron 3 Nano 4B that writes *idiomatic, performable* radio scripts
in our schema on the first try. Guided decoding already guarantees validity;
the LoRA buys genre voice, pacing, castability, and SFX/music prompt quality.

## Dataset spec

- **Size**: 400 examples. 60 per genre × 6 genres + 40 adversarial.
- **Format**: chat SFT — `[system: writer prompt w/ genre pack][user: premise]
  [assistant: schema-valid JSON]`. Assistant turn is raw JSON, no markdown
  fences (the runtime never sees fences; don't train them in).
- **Premise diversity** (per genre, roughly even):
  - object-centric ("a phone that only receives calls from 1962")
  - relationship-centric ("two rival lighthouse keepers, one light")
  - place-centric ("the last petrol pump before the salt flats")
  - one-word premises ("static", "monsoon") — users will do this
  - overlong rambling premises (train truncation-to-essence behavior)
- **Adversarial 40**: premises that tempt rule-breaking — real celebrity
  names (writer must genericize), gore-bait (must go atmospheric), requests
  for 10-minute epics (must compress), non-English premises (Hinglish for
  RAAT, translate-then-write otherwise).
- **hindi_melodrama**: dialogue in romanized Hinglish ONLY (Kokoro constraint).
  Filmi idiom: "Kasam khao!", interrupted weddings, lost-and-found lockets.

## Generation procedure (Codex-attributed — this is prize evidence)

1. `modal/gen_dataset.py` drives generation: golden fixture for the genre as
   the style exemplar + premise from a seeded premise bank (write ~70
   premises/genre by hand+Codex; cheap, high-leverage).
2. Generator model: anything goes at dev time (no param cap on tooling) —
   use the strongest model available to you; quality of training data is
   the single biggest lever in this project.
3. **Validation gauntlet** (auto-reject):
   - `Script.model_validate()` + cross-field checks from SCHEMA.md
   - runtime estimate 55–95s
   - voice-roster distinctness: no two cast members share a VoiceID
   - banned-content regex (slurs list, explicit terms)
   - dedupe: title trigram overlap <0.6 vs accepted set
4. **Human pass**: skim 5 random samples/genre. You are checking for
   *performability* — read a scene aloud. If it's stilted, fix the genre
   pack prompt, regen that genre. Budget: 45 minutes, no more.
5. Publish dataset to HF Hub (`ik-labs/midnight-static-scripts`) — this is
   the "Sharing is Caring" bonus criterion and blog material.

## Training runbook (`modal/finetune.py`)

- **Framework**: Unsloth, QLoRA 4-bit base, LoRA r=16, alpha=32,
  dropout=0.05, target modules: q,k,v,o,gate,up,down projections.
- **Hyperparams**: 3 epochs, lr 2e-4 cosine, batch 8 (grad-accum to fit),
  max_seq_len 4096, packing off (examples are whole conversations).
- **Hardware**: Modal A100-40GB, expected wall time ≤45 min, cost ≈ a few
  dollars of the $250 credit.
- **Outputs**: LoRA adapter → HF Hub `ik-labs/midnight-static-writer`
  (public — Bonus Quest "Well-Tuned" criterion) + merged fp16 checkpoint
  pushed privately for vLLM serving (vLLM can serve base+adapter too;
  merged is simpler on ZeroGPU — decide by what cold-starts faster).

## Eval (30 minutes, not a research project)

Hold out 5 premises/genre (never in training). For base vs LoRA, score:

1. **Validity-unassisted**: generate WITHOUT guided decoding; % parseable.
   (Proxy for how hard the grammar must fight the model. LoRA should
   roughly double base.)
2. **Performability spot-check**: render 6 broadcasts (one per genre)
   end-to-end; listen. Ship/no-ship is a vibe call — you have ears.
3. **Roster discipline**: % scripts using ≥2 distinct deliveries and a
   narrator-type. LoRA target ≥80%.

If LoRA underperforms base on listening test → ship base + grammar, keep the
published dataset/adapter for the Bonus Quest narrative, and say so honestly
in the blog (negative results are good Field Notes content).

## Day-1 dependency

VoiceID enum must be verified against installed Kokoro voices BEFORE dataset
generation. The LoRA memorizes the roster; a renamed voice after training =
silent miscasting forever.