---
language:
- en
library_name: peft
pipeline_tag: text-generation
license: apache-2.0
base_model: Qwen/Qwen3.5-0.8B
tags:
- lora
- sft
- trl
- script-generation
- minimax-h3
- video-generation
---

# H3-ScriptGen — MiniMax-H3 FL2VA scriptwriting LoRA

A PEFT LoRA adapter on **Qwen/Qwen3.5-0.8B** that writes **stage/camera directions for MiniMax H3** — one FL2VA scene beat per request, in the exact field structure the H3 video pipeline consumes (`ACTION`, `SHOT`, `STORYBOARD_PROMPT`, `H3_MODE: FL2VA`, `H3_VIDEO_PROMPT`, `overall_soundscape`, `non_diegetic_music`, `DURATION`). Each beat maps 1:1 to a storyboard still + one ~5 s H3 FL2V clip.

This is the **merged** adapter: it was **continue-trained from the previous `final/` adapter** (story craft + TVTropes) on 836 H3-format SFT rows, so it keeps the old screenplay/trope knowledge and adds the MiniMax-H3 prompt format on top. Per the project's own guidance, that continue-train is the "practical merge of skills" (see `docs/H3_FORMAT_README.md`) — it is *not* a weighted merge of two separately-trained LoRAs.

## Contents

| Artifact | Description |
|---|---|
| `adapter_model.safetensors` + `adapter_config.json` | **The merged adapter (final, epoch 2)** — load with PEFT |
| `tokenizer_config.json`, `tokenizer.json`, `chat_template.jinja` | Qwen3.5 tokenizer + chat template (from base) |
| `training_config.json` | Training metadata (`init_from: …/final`, base, hyperparams) |
| `checkpoint-105/` | Epoch-1 checkpoint (full trainer state, resumable) |
| `checkpoint-210/` | Epoch-2 checkpoint (== root adapter; full trainer state) |
| `scripts/` | `train_script_lora_h3.py`, `build_sft_from_scriptlib.py`, SFT dataset (`train_dataset.full.jsonl`, 836 rows) + seed examples |
| `docs/` | MiniMax H3 prompt guides (`VIDEO_PROMPT_base-en.txt`, `VIDEO_PROMPT_ref-en.txt`), `h3_prompt_format.py` (runtime field builders), `H3_FORMAT_README.md` |

## Base model

- **`Qwen/Qwen3.5-0.8B`** (Apache-2.0), 0.8B params, causal LM.
- LoRA: `r=16`, `alpha=32`, `dropout=0.1`, target modules `q/k/v/o_proj` + `gate/up/down_proj` (193 tensors, 193 = standard PEFT layout).

## Training

| Setting | Value |
|---|---|
| Format | `minimax-h3-fl2va-v1` (SFT, chat template) |
| Init | Continue-train from `models/script-lora/final` adapter |
| Data | `train_dataset.full.jsonl` — 836 rows from 102 screenplays (`scriptlib`) + TVTropes seeds + hand-written H3 examples |
| Epochs / steps | 2 / 210 |
| Learning rate | 1e-4 (cosine decay) |
| Max seq len | 1536 |
| Optimizer | AdamW (non-fused, XPU) |
| Device | Intel Arc A770 (XPU) |

Final metrics (from training log): `train_loss 0.7535`, final-step `mean_token_accuracy 0.8932`; token accuracy ranged ~0.86–0.91 over the run. `checkpoint-105` (epoch 1) and `checkpoint-210` (epoch 2) are both included; the root `adapter_model.safetensors` is identical to `checkpoint-210`.

## Output format (one beat)

```text
## SCENE {N} — {SLUGLINE}
ACTION: <1–2 sentences of visual action for ~5 s>
DIALOGUE — {NAME}: <line>            (at most 1 line, or omit if silent)
SHOT: <camera type + optional amplitude + speed, natural English>
STORYBOARD_PROMPT: <self-contained still-image prompt; no camera timeline>
H3_MODE: FL2VA
H3_VIDEO_PROMPT:
How the reference pictures align with the target video — Picture 1 (from Shot 1) aligns with the 0.00-second mark of the target video; Picture 2 (from Shot 1) aligns with the 5.00-second mark of the target video.

integrated_multimodal_description: [Shot 1] Live-action, cinematic, <opening composition matching the storyboard>. <continuous motion path Picture 1 → Picture 2; camera motion as natural English>. <dialogue as: the {name} (S1) says: <d>[English] line here</d>>

overall_soundscape: <ambience / physical sounds, or N/A>

non_diegetic_music: <audience-only score, or N/A>
LORA: <image-lora:strength, or "none">
AUDIO: <post-process sfx/music note, or "none">
DURATION: 5
```

The `h3_video_prompt` and `storyboard_prompt` fields feed directly into the MiniMax H3 FL2V pipeline (storyboard panel N → panel N+1, `zvideo_h3_storyboard_fl2v`).

## Usage

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "Qwen/Qwen3.5-0.8B"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16")
model = PeftModel.from_pretrained(model, "woodfireind/H3-ScriptGen")

messages = [
    {"role": "system", "content": "You write ONE MiniMax-H3 FL2VA scene beat for Backlot."},
    {"role": "user", "content": "Premise: A courier delivers a package through a neon alley in the rain.\nWrite SCENE 1 now."},
]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
out = tok.generate(tok(text, return_tensors="pt").input_ids, max_new_tokens=512)
print(tok.decode(out[0]))
```

The adapter also loads on llama.cpp / vLLM servers that support PEFT LoRA adapters on the same base model.

## Limitations

- **Small base (0.8B)** — strong on structure/format adherence; weaker than larger models on nuance, and token accuracy is ~0.9, so occasional malformed fields are expected. Validate output with `docs/h3_prompt_format.py` (`parse_scene_h3_fields`).
- **Text-only.** This adapter only produces prompt text. Producing video still requires the MiniMax H3 stack (GGUF DiT + VAE + text encoder); on this project's local stack the H3 pipeline has **no audio** (audio VAE dropped) and cut timing can drift ±2 s.
- **H3 prompt rules are exacting.** `H3_VIDEO_PROMPT` must keep the FL2VA alignment line, `<d>[Language] …</d>` dialogue tags, and speaker `(S1)` IDs. See `docs/VIDEO_PROMPT_base-en.txt`.
- **Training-data provenance.** The SFT set was built from an internal 102-screenplay corpus + TVTropes metadata + hand-written examples. Review rights before commercial redistribution of generated content.
- The H3 prompt-field structure follows MiniMax's public H3 prompt guides; using it to generate videos is subject to the MiniMax H3 Community License Agreement.

## License

Adapter weights are Apache-2.0 (matching the Qwen3.5-0.8B base). Training data is from an internal screenplay corpus — see provenance note above.