README.md

---
base_model: vidore/ColSmolVLM-base
library_name: peft
tags:
- vidore-experimental
- vidore
---

# ColSmolVLM-alpha: Visual Retriever based on SmolVLM-Instruct with ColBERT strategy

### This is a version trained with batch_size 128 for 3 epochs

ColSmolVLM 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 SmolVLM 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)

This version is the untrained base version to guarantee deterministic projection layer initialization.
<p align="center"><img width=800 src="https://github.com/illuin-tech/colpali/blob/main/assets/colpali_architecture.webp?raw=true"/></p>

## Version specificity

This version is trained with `colpali-engine==0.3.5`. (main branch from the repo)

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


## 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 and most probably in the multimodal training.*

### Parameters

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 a 4 GPU setup with data parallelism, a learning rate of 5e-4 with linear decay with 2.5% warmup steps, and a batch size of 32.

## Usage

Make sure `colpali-engine` is installed from source or with a version superior to 0.3.5 (main branch from the repo currently).
`transformers` version must be > 4.46.2.

```bash
pip install git+https://github.com/illuin-tech/colpali
```

```python
import torch
from PIL import Image

from colpali_engine.models import ColIdefics3, ColIdefics3Processor

model = ColIdefics3.from_pretrained(
        "vidore/colsmolvlm-alpha",
        torch_dtype=torch.bfloat16,
        device_map="cuda:0",
        attn_implementation="flash_attention_2" # or eager
    ).eval()
processor = ColIdefics3Processor.from_pretrained("vidore/colsmolvlm-alpha")

# 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)
```


## 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

ColQwen2's vision language backbone model (Qwen2-VL) is under `apache2.0` license. 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:

```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}, 
}
```