Colpali V1.3 Hf

🚀 ColPali: Visual Retriever based on PaliGemma-3B with ColBERT strategy

ColPali is a model leveraging a novel architecture and training strategy based on Vision Language Models (VLMs). It can efficiently index documents using their visual features. It extends PaliGemma-3B to generate ColBERT-style multi-vector representations of text and images. This model was introduced in the paper ColPali: Efficient Document Retrieval with Vision Language Models and first released in this repository. The HuggingFace transformers 🤗 implementation was contributed by Tony Wu (@tonywu71) and Yoni Gozlan (@yonigozlan).

🚀 Quick Start

⚠️ Important Note

This version of ColPali should be loaded with the transformers 🤗 release, not with colpali-engine. It was converted using the convert_colpali_weights_to_hf.py script from the vidore/colpali-v1.3-merged checkpoint.

✨ Features

• Based on Vision Language Models (VLMs), it can efficiently index documents from visual features.
• It is an extension of PaliGemma-3B, generating ColBERT-style multi-vector representations of text and images.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

import torch
from PIL import Image

from transformers import ColPaliForRetrieval, ColPaliProcessor

model_name = "vidore/colpali-v1.3-hf"

model = ColPaliForRetrieval.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="cuda:0",  # or "mps" if on Apple Silicon
).eval()

processor = ColPaliProcessor.from_pretrained(model_name)

# Your inputs
images = [
    Image.new("RGB", (32, 32), color="white"),
    Image.new("RGB", (16, 16), color="black"),
]
queries = [
    "What is the organizational structure for our R&D department?",
    "Can you provide a breakdown of last year’s financial performance?",
]

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

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

# Score the queries against the images
scores = processor.score_retrieval(query_embeddings.embeddings, image_embeddings.embeddings)

📚 Documentation

Model Description

Read the transformers 🤗 model card: https://huggingface.co/docs/transformers/en/model_doc/colpali.

Model Training

Dataset

Our training dataset consists of 127,460 query - page pairs. It includes train sets from openly available academic datasets (63%) and a synthetic dataset composed of pages from web - crawled PDF documents, augmented with VLM - generated (Claude - 3 Sonnet) pseudo - questions (37%). The training set is fully English, allowing us to study zero - shot generalization to non - English languages. We ensure no multi - page PDF document is used in both ViDoRe and the train set to prevent evaluation contamination. A validation set is created with 2% of the samples for hyperparameter tuning.

Note: Multilingual data is present in the pretraining corpus of the language model (Gemma - 2B) and may occur during PaliGemma - 3B's multimodal training.

Parameters

All models are trained for 1 epoch on the train set. Unless specified otherwise, we train models in bfloat16 format, use low - rank adapters (LoRA) with alpha = 32 and r = 32 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 8 GPU setup with data parallelism, a learning rate of 5e - 5 with linear decay and 2.5% warmup steps, and a batch size of 32.

🔧 Technical Details

The model is based on Vision Language Models (VLMs) and uses a novel architecture and training strategy. It extends PaliGemma-3B and generates ColBERT-style multi-vector representations of text and images.

📄 License

ColPali's vision language backbone model (PaliGemma) is under gemma license as specified in its model card. ColPali inherits from this gemma license.

Resources

• The ColPali arXiv paper can be found here. 📄
• The official blog post detailing ColPali can be found here. 📝
• The original model implementation code for the ColPali model and for the colpali-engine package can be found here. 🌎
• Cookbooks for learning to use the transformers - native version of ColPali, fine - tuning, and similarity maps generation can be found here. 📚

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.

Contact

• Manuel Faysse: manuel.faysse@illuin.tech
• Hugues Sibille: hugues.sibille@illuin.tech
• Tony Wu: tony.wu@illuin.tech

Citation

If you use any datasets or models from this organization in your research, please cite the original dataset 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}, 
}