---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: peft
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
- lora
- qwen2_5_vl
- vision-language-model
- document-understanding
- receipt-extraction
datasets:
- sarcasticcoder/cord-extraction-lora
---

# Qwen2.5-VL-7B - CORD structured-extraction LoRA

QLoRA adapter that fine-tunes **Qwen/Qwen2.5-VL-7B-Instruct** to read a document
image and emit structured JSON (receipt fields + line items), trained on a small
CORD subset.

- **Base:** `Qwen/Qwen2.5-VL-7B-Instruct`
- **Method:** QLoRA (4-bit base, LoRA r=16 on the language attention/MLP layers,
  vision tower frozen) via Unsloth, 2 epochs on 200 CORD train receipts.
- **Target:** the canonical extraction schema (doc_type, receipt fields, line items).

## Result - CORD test[:40], same pipeline, 4-bit

| | Field EM | Field F1 | Line-item F1 |
|---|---|---|---|
| Zero-shot v3 prompt | 0.869 | 0.834 | 0.934 |
| This LoRA | 0.865 | 0.830 | **0.957** |

Matches the already-strong prompt baseline on fields and lifts line-item F1 +0.02.

## Usage
```python
from peft import PeftModel
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
base = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", device_map="auto")
model = PeftModel.from_pretrained(base, "sarcasticcoder/qwen2.5-vl-7b-cord-lora")
proc = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
```