metadata
language: en
tags:
- reranker
- RAG
- multimodal
- vision-language
- Qwen
license: cc-by-4.0
pipeline_tag: visual-document-retrieval
DocReRank: Multi-Modal Reranker
This is the official model from the paper:
📄 DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers
✅ Model Overview
- Base model: Qwen/Qwen2-VL-2B-Instruct
- Architecture: Vision-Language reranker
- Fine-tuning method: PEFT (LoRA)
- Training data: Generated by Single-Page Hard Negative Query Generation Pipeline.
- Purpose: Improves second-stage reranking for Retrieval-Augmented Generation (RAG) in multimodal scenarios.
✅ How to Use
This adapter requires the base Qwen2-VL model.
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from peft import PeftModel
import torch
from PIL import Image
# Load base model
base_model = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-2B-Instruct",
torch_dtype=torch.bfloat16,
device_map="cuda"
)
# Load DocReRank adapter
model = PeftModel.from_pretrained(base_model, "DocReRank/DocReRank-Reranker").eval()
# Load processor
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
# Example query and image
query = "What is the total revenue in the table?"
image = Image.open("sample_page.png")
inputs = processor(text=query, images=image, return_tensors="pt").to("cuda", torch.bfloat16)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=16)
print(processor.tokenizer.decode(outputs[0], skip_special_tokens=True))