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qwen2.5-vl-7b-cord-lora
sarcasticcoder/qwen2.5-vl-7b-cord-lora
qwen2.5-vl-7b-cord-lora is a image-text-to-text model from sarcasticcoder. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
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.
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At a glance
- Task
- Image-Text-to-Text
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Jun 14, 2026
- Updated
- Jun 14, 2026
- SHA
- 28ef79e7
Try a prompt
Base models
Model card
From the Hugging Face model README
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
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")
- Task
- Image-Text-to-Text
- Library
- peft
- License
- apache-2.0
- Created
- Jun 14, 2026
- Updated
- Jun 14, 2026