Colqwen2.5 3b Multilingual V1.0

🚀 ColQwen2.5-3b-multilingual-v1.0: Multilingual Visual Retriever based on Qwen2.5-VL-3B-Instruct with ColBERT strategy

ColQwen2.5-3b-multilingual-v1.0 is a multilingual visual retriever. It uses a novel architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features.

🚀 Quick Start

Ranked #1 among models smaller than 7B parameters and #2 overall on the Vidore benchmark (as of February 11, 2025). The reported scores are on the Vidore Leaderboard.

This is the base version trained on 4xA100 80GB with per_device_batch_size = 128 and gradient_accumulation_steps = 2 for 5 epochs.

ColQwen is a model based on a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features. It is a Qwen2.5-VL-3B extension that generates ColBERT- style multi-vector representations of text and images. It was introduced in the paper ColPali: Efficient Document Retrieval with Vision Language Models and first released in this repository

✨ Features

Version specificity

This model takes dynamic image resolutions in input and does not resize them, changing their aspect ratio as in ColPali. Maximal resolution is set so that 768 image patches are created at most. Experiments show clear improvements with larger amounts of image patches, at the cost of memory requirements.

This version is trained with colpali-engine==0.3.7.

Data

• Synthetic data: Selected and preprocessed from the openbmb/VisRAG-Ret-Train-Synthetic-data dataset.
• In-domain VQA dataset: Drawn from openbmb/VisRAG-Ret-Train-In-domain-data.
• Docmatix dataset: Extracted from the Metric-AI/rag_docmatix_100k dataset.
• Colpali dataset: Taken from vidore/colpali_train_set.
• Multilingual dataset: Taken from llamaindex/vdr-multilingual-train.

Model Training

Parameters

We train models using low-rank adapters (LoRA) with alpha = 128 and r = 128 on the transformer layers from the language model, as well as the final randomly initialized projection layer, and use a paged_adamw_8bit optimizer. We train on an 4xA100 GPU setup with distributed data parallelism (via accelerate), a learning rate of 2e - 4 with linear decay with 1% warmup steps, batch size per device is 128, gradient accumulation steps are 2, in bfloat16 format

📦 Installation

Make sure colpali-engine is installed from source or with a version superior to 0.3.1. transformers version must be > 4.45.0.

ColPali

pip install git+https://github.com/illuin-tech/colpali

or

pip install git+https://github.com/illuin-tech/colpali@colqwen2_5

Qwen2.5

The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:

pip install git+https://github.com/huggingface/transformers accelerate

or you might encounter the following error:

KeyError: 'qwen2_5_vl'

💻 Usage Examples

Basic Usage

import torch
from PIL import Image

from colpali_engine.models import ColQwen2_5, ColQwen2_5_Processor

model = ColQwen2_5.from_pretrained(
        "Metric-AI/ColQwen2.5-3b-multilingual-v1.0",
        torch_dtype=torch.bfloat16,
        device_map="cuda:0",  # or "mps" if on Apple Silicon
    ).eval()
processor = ColQwen2_5_Processor.from_pretrained("Metric-AI/ColQwen2.5-3b-multilingual-v1.0")

# Your inputs
images = [
    Image.new("RGB", (32, 32), color="white"),
    Image.new("RGB", (16, 16), color="black"),
]
queries = [
    "Is attention really all you need?",
    "What is the amount of bananas farmed in Salvador?",
]

# Process the inputs
batch_images = processor.process_images(images).to(model.device)
batch_queries = processor.process_queries(queries).to(model.device)

# Forward pass
with torch.no_grad():
    image_embeddings = model(**batch_images)
    query_embeddings = model(**batch_queries)

scores = processor.score_multi_vector(query_embeddings, image_embeddings)

📚 Documentation

Limitations

• Focus: The model primarily focuses on PDF-type documents and high-ressources languages, potentially limiting its generalization to other document types or less represented languages.
• Support: The model relies on multi-vector retreiving derived from the ColBERT late interaction mechanism, which may require engineering efforts to adapt to widely used vector retrieval frameworks that lack native multi-vector support.

📄 License

ColQwen2.5's vision language backbone model (Qwen2.5-VL) is under apache2.0 license. The adapters attached to the model are under MIT license.

📚 Documentation

Citation

If you use this models from this organization in your research, please cite the original paper as follows:

@misc{faysse2024colpaliefficientdocumentretrieval,
  title={ColPali: Efficient Document Retrieval with Vision Language Models}, 
  author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
  year={2024},
  eprint={2407.01449},
  archivePrefix={arXiv},
  primaryClass={cs.IR},
  url={https://arxiv.org/abs/2407.01449}, 
}

Developed by

Metric AI Research Lab