Transformers
Safetensors
ColPali
English
pretraining
Inference Endpoints
colpali-v1.2-hf / README.md
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---
library_name: transformers
tags:
- colpali
license: gemma
datasets:
- vidore/colpali_train_set
language:
- en
base_model:
- vidore/colpaligemma-3b-pt-448-base
new_version: vidore/colpali-v1.3-hf
---
> [!IMPORTANT]
> 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](https://github.com/tonywu71/transformers/blob/21c1309637aee97ca4fb8eb3b31830913a0f99a5/src/transformers/models/colpali/convert_colpali_weights_to_hf.py)
> from the [`vidore/colpali-v1.2-merged`](https://huggingface.co/vidore/colpali-v1.2-merged) checkpoint.
# 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)
The HuggingFace `transformers` 🤗 implementation was contributed by Tony Wu ([@tonywu71](https://huggingface.co/tonywu71)) and Yoni Gozlan ([@yonigozlan](https://huggingface.co/yonigozlan)).
<p align="center"><img width=800 src="https://github.com/illuin-tech/colpali/blob/main/assets/colpali_architecture.webp?raw=true"/></p>
## 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
from PIL import Image
from transformers import ColPaliForRetrieval, ColPaliProcessor
model_name = "vidore/colpali-v1.2-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)
```
## 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},
}
```