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

This is a multilingual visual retriever model. It is based on the Qwen2.5-VL-3B-Instruct model and uses the ColBERT strategy. It can efficiently index documents from their visual features.

Key Information

Model Introduction

This is the base version trained on 8xH100 80GB with per_device_batch_size = 128 for 8 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. The 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==0.3.9.

📦 Installation

pip install git+https://github.com/illuin-tech/colpali
pip install transformers==4.49.0
pip install flash-attn --no-build-isolation

💻 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(
        "tsystems/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("tsystems/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

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 8xH100 GPU setup with distributed data parallelism (via accelerate), a learning rate of 2e - 4 with linear decay with 1% warmup steps, and the batch size per device is 128 in bfloat16 format.

Limitations

• Focus: The model primarily focuses on PDF - type documents and high - resources languages, potentially limiting its generalization to other document types or less represented languages.
• Support: The model relies on multi - vector retrieving 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.

📚 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: T-Systems International