docs/H3_FORMAT_README.md

Script-LoRA → MiniMax H3 format

Data locations

Path	Role
../scriptlib/	102 screenplays (.txt) — original training library
../TVTropesData/	Trope/title tables for scenario variety
../final/	Existing PEFT adapter (story craft + tropes, classic screenplay style)
train_dataset.jsonl	Hand-written H3 seed examples
train_dataset.full.jsonl	Built from scriptlib (+ tropes + seed)

Recommended path (continue-train, not merge-from-scratch)

The existing final/ adapter already holds script + TVTropes knowledge. You do not need a LoRA merge of two adapters unless you train a separate format-only adapter. Prefer:

cd /home/bbear/Documents/OlympusServer/models/script-lora/h3-format

# Use the OlympusServer venv (system python lacks datasets/trl/peft)
VENV=/home/bbear/Documents/OlympusServer/.venv/bin/python

# 1) Build SFT rows from scriptlib (rewrite + premise) + TVTropes seeds
$VENV build_sft_from_scriptlib.py \
  --include-seed \
  --chunks-per-script 4 \
  --tropes 80 \
  --out train_dataset.full.jsonl

# 2) Continue-train from final/ → h3-v1 (does not overwrite final/)
# This host: torch is XPU-only → use --device xpu (not cuda)
$VENV train_script_lora_h3.py \
  --dataset train_dataset.full.jsonl \
  --init-from ../final \
  --epochs 2 \
  --lr 1e-4 \
  --device xpu

Output: ../h3-v1/. Point the generation slot at that adapter after validation.

Why not “merge” alone?

• Weighted LoRA merge needs two trained adapters. Today you have one (final/).
• Continue-train from final/ on H3-format chat data is the practical merge of skills: old weights + new format supervision.
• Optional later: train a thin format-only LoRA and add_weighted_adapter with final/.

Runtime (already live without retrain)

Backlot system prompts emit H3 FL2VA fields regardless of adapter:

• backend/services/h3_prompt_format.py
• /generate/script, /generate/script/scene → h3_video_prompt, storyboard_prompt

Guides: optimization/h3-shrink/docs/VIDEO_PROMPT_{base,ref}-en.txt