--- 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.
## 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 CĂ©line Hudelot and Pierre Colombo}, year={2024}, eprint={2407.01449}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2407.01449}, } ```