tomaarsen/colpali-v1.3-st

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

This version is trained with 256 batch size for 3 epochs on the same data as the original ColPali model.

ColPali 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 PaliGemma-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

Version specificity

This version is trained with colpali-engine==0.2.0 but can be loaded for any version >=0.2.0.

Compared to vidore/colpali, this version is trained with right padding for queries to fix unwanted tokens in the query encoding. It also stems from the fixed vidore/colpaligemma-3b-pt-448-base to guarantee deterministic projection layer initialization. It was trained for 5 epochs, with in-batch negatives and hard mined negatives and a warmup of 1000 steps (10x longer) to help reduce non-english language collapse.

Data is the same as the ColPali data described in the paper.

Model Description

This model is built iteratively starting from an off-the-shelf SigLIP model. We finetuned it to create BiSigLIP and fed the patch-embeddings output by SigLIP to an LLM, PaliGemma-3B to create BiPali.

One benefit of inputting image patch embeddings through a language model is that they are natively mapped to a latent space similar to textual input (query). This enables leveraging the ColBERT strategy to compute interactions between text tokens and image patches, which enables a step-change improvement in performance compared to BiPali.

Model Training

Dataset

Our training dataset of 127,460 query-page pairs is comprised of train sets of openly available academic datasets (63%) and a synthetic dataset made up of pages from web-crawled PDF documents and augmented with VLM-generated (Claude-3 Sonnet) pseudo-questions (37%). Our training set is fully English by design, enabling us to study zero-shot generalization to non-English languages. We explicitly verify no multi-page PDF document is used both ViDoRe and in the train set to prevent evaluation contamination. A validation set is created with 2% of the samples to tune hyperparameters.

Note: Multilingual data is present in the pretraining corpus of the language model (Gemma-2B) and potentially occurs 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 with 2.5% warmup steps, and a batch size of 32.

Usage

Using Sentence Transformers

ColPali can be used as a multi-vector (ColBERT-style late interaction) retriever directly with Sentence Transformers via the MultiVectorEncoder. Text queries and document page images are encoded into token-level embeddings and scored with MaxSim.

Support for ColPali relies on the MultiVectorEncoder, which has not yet been included in a stable Sentence Transformers release as of writing. Until it is, install Sentence Transformers from source:

pip install "sentence-transformers[image] @ git+https://github.com/huggingface/sentence-transformers.git" peft

from sentence_transformers import MultiVectorEncoder

# trust_remote_code=True lets Sentence Transformers load the LoRA adapter onto its PaliGemma base
model = MultiVectorEncoder("tomaarsen/colpali-v1.3-st", trust_remote_code=True)

queries = [
    "What is the variable represented on the y-axis of the graph?",
    "Total outlay is maximum in which year?",
]
images = [
    "https://huggingface.co/tomaarsen/colpali-v1.3-st/resolve/main/assets/doc1.jpg",
    "https://huggingface.co/tomaarsen/colpali-v1.3-st/resolve/main/assets/doc2.jpg",
    "https://huggingface.co/tomaarsen/colpali-v1.3-st/resolve/main/assets/doc3.jpg",
    "https://huggingface.co/tomaarsen/colpali-v1.3-st/resolve/main/assets/doc4.jpg",
]

# Queries are text (the visual prompt + query augmentation buffer are applied automatically),
# documents are page images.
query_embeddings = model.encode_query(queries, convert_to_tensor=True)
document_embeddings = model.encode_document(images, convert_to_tensor=True)
print(f"Query 0 shape:    {tuple(query_embeddings[0].shape)}")
print(f"Document 0 shape: {tuple(document_embeddings[0].shape)}")
# Query 0 shape:    (25, 128)
# Document 0 shape: (1031, 128)

# MaxSim late-interaction scoring (rows = queries, columns = images)
scores = model.similarity(query_embeddings, document_embeddings)
print(scores)
# tensor([[19.5000, 17.5000, 17.3750, 16.7500],
#         [ 5.5000, 11.1250,  5.4062,  6.4062]], dtype=torch.bfloat16)

Using ColPali Engine

Install colpali-engine:

pip install colpali-engine>=0.3.0,<0.4.0

Then run the following code:

from typing import cast

import torch
from PIL import Image

from colpali_engine.models import ColPali, ColPaliProcessor

model_name = "vidore/colpali-v1.3"

model = ColPali.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 = [
    "Is attention really all you need?",
    "Are Benjamin, Antoine, Merve, and Jo best friends?",
]

# 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)

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

ColPali's vision language backbone model (PaliGemma) is under gemma license as specified in its model card. The adapters attached to the model are under MIT license.

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}, 
}