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----
-license: mit
-library_name: colpali
-language:
-- en
-tags:
-- vidore
----
-# ColPali: Visual Retriever based on PaliGemma-3B with ColBERT strategy
-
-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](https://huggingface.co/google/paligemma-3b-mix-448) extension that generates [ColBERT](https://arxiv.org/abs/2004.12832)- style multi-vector representations of text and images. 
-It was introduced in the paper [ColPali: Efficient Document Retrieval with Vision Language Models](https://arxiv.org/abs/2407.01449) and first released in [this repository](https://github.com/ManuelFay/colpali)
-
-## Model Description
-
-This model is built iteratively starting from an off-the-shelf [SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) model. 
-We finetuned it to create [BiSigLIP](https://huggingface.co/vidore/bisiglip) and fed the patch-embeddings output by SigLIP to an LLM, [PaliGemma-3B](https://huggingface.co/google/paligemma-3b-mix-448) to create [BiPali](https://huggingface.co/vidore/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](https://arxiv.org/abs/2004.12832) 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*](https://huggingface.co/collections/vidore/vidore-benchmark-667173f98e70a1c0fa4db00d) 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](https://arxiv.org/abs/2106.09685)) 
-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
-
-```python
-import torch
-import typer
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-from transformers import AutoProcessor
-from PIL import Image
-
-from colpali_engine.models.paligemma_colbert_architecture import ColPali
-from colpali_engine.trainer.retrieval_evaluator import CustomEvaluator
-from colpali_engine.utils.colpali_processing_utils import process_images, process_queries
-from colpali_engine.utils.image_from_page_utils import load_from_dataset
-
-
-def main() -> None:
-    """Example script to run inference with ColPali"""
-
-    # Load model
-    model_name = "vidore/colpali"
-    model = ColPali.from_pretrained("google/paligemma-3b-mix-448", torch_dtype=torch.bfloat16, device_map="cuda").eval()
-    model.load_adapter(model_name)
-    processor = AutoProcessor.from_pretrained(model_name)
-
-    # select images -> load_from_pdf(<pdf_path>),  load_from_image_urls(["<url_1>"]), load_from_dataset(<path>)
-    images = load_from_dataset("vidore/docvqa_test_subsampled")
-    queries = ["From which university does James V. Fiorca come ?", "Who is the japanese prime minister?"]
-
-    # run inference - docs
-    dataloader = DataLoader(
-        images,
-        batch_size=4,
-        shuffle=False,
-        collate_fn=lambda x: process_images(processor, x),
-    )
-    ds = []
-    for batch_doc in tqdm(dataloader):
-        with torch.no_grad():
-            batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}
-            embeddings_doc = model(**batch_doc)
-        ds.extend(list(torch.unbind(embeddings_doc.to("cpu"))))
-
-    # run inference - queries
-    dataloader = DataLoader(
-        queries,
-        batch_size=4,
-        shuffle=False,
-        collate_fn=lambda x: process_queries(processor, x, Image.new("RGB", (448, 448), (255, 255, 255))),
-    )
-
-    qs = []
-    for batch_query in dataloader:
-        with torch.no_grad():
-            batch_query = {k: v.to(model.device) for k, v in batch_query.items()}
-            embeddings_query = model(**batch_query)
-        qs.extend(list(torch.unbind(embeddings_query.to("cpu"))))
-
-    # run evaluation
-    retriever_evaluator = CustomEvaluator(is_multi_vector=True)
-    scores = retriever_evaluator.evaluate(qs, ds)
-    print(scores.argmax(axis=1))
-
-
-if __name__ == "__main__":
-    typer.run(main)
-
-```
-
-## 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](https://huggingface.co/google/paligemma-3b-mix-448). The adapters attached to the model are under MIT license.
-
-## Contact
-
-- Manuel Faysse: [email protected]
-- Hugues Sibille: [email protected]
-- Tony Wu: [email protected]
-
-## Citation
-
-If you use any datasets or models from this organization in your research, please cite the original dataset as follows:
-
-```bibtex
-@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}, 
-}
-```