---
base_model: Qwen/Qwen3.5-9B
library_name: peft
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
- lora
- peft
- presentation
- slide-editing
- visual-qa
---

# EditPPT Vision Validator (LoRA on Qwen3.5-9B)

A LoRA adapter that checks whether an edit to a PowerPoint slide **newly introduced a severe visual defect**,
and if so, returns a repair as tool calls — in one generation. It serves as the vision validator of EditPPT.

Given the slide rendered **before** and **after** an edit, plus the edit instruction, the tool calls that made the
edit and the slide's shape JSON, the model returns:

```json
{"HasCriticalIssues": "Yes",
 "Issues": [{"IssueType": "ELEMENT_OVERLAP", "AffectedShapeIDs": [2],
             "TechnicalDiagnosis": "textbox 2 overlaps the caption below it by about 20 pt"}],
 "ToolCalls": [{"name": "adjust_layout", "arguments": {"slide_number": 3, "shape_id": 2, "top": 470}}]}
```

* **Defect types:** `CONTENT_OFF_SLIDE`, `ELEMENT_OVERLAP`, `LOW_CONTRAST`.
* **Repair tools:** `set_shape_font_size`, `adjust_layout`, `set_text_style`. Values are absolute targets in slide points.
* Defects already present in the BEFORE image are treated as the deck's own design and are not reported.

## Usage

See `inference_example.py` and `prompt_template.txt`.

* Message order: `[image BEFORE, image AFTER, prompt text]`.
* Images: 960x540 (`min_pixels = max_pixels = 960*540`). One slide point = one pixel for a 16:9 deck.
* Greedy decoding, default chat template.

## Training

| | |
|---|---|
| Base | Qwen3.5-9B, bf16 |
| Adapter | LoRA, r 16, alpha 32, on the language model and the vision encoder |
| Data | 7,260 training samples (plus 374 for validation) with human-confirmed labels, from publicly licensed decks (CC0 / CC-BY) |

## Evaluation

Held-out set of 750 samples from decks not seen in training:

| Metric | Value |
|---|---|
| Verdict accuracy, edits without a repair (n=497) | 0.932 (base model: 0.60) |
| Verdict accuracy, all 750 samples | 0.955 |
| Defect precision / recall / F1, all 750 samples | 0.963 / 0.954 / 0.959 |
| Repair targets the right shapes with a permitted tool, all 750 samples | 0.948 |

## License

Apache-2.0, following the base model.