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ApsaraStackMaaS/
EvoQwen2.5-VL-Retriever-7B-v1

Name: EvoQwen2.5-VL-Retriever-7B-v1
Rating: 2.5 (398 reviews)

safetensors

qwen2_5_vl

visual-document-retrieval

vidore/colpali_train_set

openbmb/VisRAG-Ret-Train-Synthetic-data

openbmb/VisRAG-Ret-Train-In-domain-data

2603.16455

Qwen/Qwen2.5-VL-7B-Instruct

finetune

apache-2.0

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Model Card for Model ID

EvoQwen2.5-VL-Retriever-7B-v1 is a high-performance multimodal retrieval model built upon the Qwen2.5-VL-7B-Instruct backbone and employing multi-vector late-interaction. The model is fine-tuned by using an innovative evolutionary training framework (Evo-Retriever), enabling accurate retrieval of complex visual documents.

Version Specificity

• Base Model: ApsaraStackMaaS/EvoQwen2.5-VL-Retriever-7B-v1

• Parameter Size: 7 billion (7B)

• Features: The 7B version delivers the highest retrieval accuracy across all evaluation benchmarks, making it ideal for applications with stringent performance requirements.

Performance

Model	ViDoRe V2 (nDCG@5)	MMEB VisDoc (ndcg_linear@5)
ApsaraStackMaaS/EvoQwen2.5-VL-Retriever-3B-v1	63.00	75.96
ApsaraStackMaaS/EvoQwen2.5-VL-Retriever-7B-v1	65.24	77.10

Usage

Make sure that you have installed Transformers, Torch, Pillow, and colpali-engine.

javascript

  import torch
  from PIL import Image
  from transformers.utils.import_utils import is_flash_attn_2_available
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  images = [
      Image.new("RGB", (128, 128), color="white"),
      Image.new("RGB", (64, 32), color="black"),
  ]
  queries = [
      "Is attention really all you need?",
      "What is the amount of bananas farmed in Salvador?",
  ]
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  batch_images = processor.process_images(images).to(model.device)
  batch_queries = processor.process_queries(queries).to(model.device)
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  with torch.no_grad():
      image_embeddings = model(**batch_images)
      query_embeddings = model(**batch_queries)
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