Create README.md

README.md

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+---
+license: mit
+datasets:
+- openbmb/VisRAG-Ret-Train-Synthetic-data
+- openbmb/VisRAG-Ret-Train-In-domain-data
+- tsystems/vqa_de_en_batch1
+- vidore/colpali_train_set
+- llamaindex/vdr-multilingual-train
+- Metric-AI/tabfquad_train_set
+language:
+- en
+- fr
+- es
+- it
+- de
+base_model:
+- Qwen/Qwen2.5-VL-3B-Instruct
+tags:
+- multimodal_embedding
+- multilingual_embedding
+- Text-to-Visual Document (T→VD) retrieval
+library_name: peft
+pipeline_tag: visual-document-retrieval
+---
+# ColQwen2.5-3b-multilingual-v1.0: Multilingual Visual Retriever based on Qwen2.5-VL-3B-Instruct with ColBERT strategy
+
+### This is the base version trained on 8xH100 80GB with per_device_batch_size=128 for 8 epoch. 
+
+ColQwen 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 [Qwen2.5-VL-3B](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) 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)
+
+<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 model takes dynamic image resolutions in input and does not resize them, changing their aspect ratio as in ColPali.
+Maximal resolution is set so that 768 image patches are created at most. Experiments show clear improvements with larger amounts of image patches, at the cost of memory requirements.
+
+This version is trained with `colpali-engine==0.3.9`.
+
+## Data
+- **German & English**: Taken from the `tsystems/vqa_de_en_batch1` dataset.
+- **Multilingual dataset**: Taken from `llamaindex/vdr-multilingual-train`.
+- **Synthetic data**: Taken from `openbmb/VisRAG-Ret-Train-Synthetic-data` dataset.  
+- **In-domain VQA dataset**: Taken from `openbmb/VisRAG-Ret-Train-In-domain-data` dataset.
+- **Colpali dataset**: Taken from `vidore/colpali_train_set`.
+
+## Model Training
+
+### Parameters
+We train models  use low-rank adapters ([LoRA](https://arxiv.org/abs/2106.09685)) 
+with `alpha=128`  and `r=128` 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 8xH100 GPU setup with distributed data parallelism (via accelerate), a learning rate of 2e-4 with linear decay with 1% warmup steps, batch size per device is 128 in `bfloat16` format
+
+## Installation
+```bash
+pip install git+https://github.com/illuin-tech/colpali
+pip install transformers==4.49.0
+pip install flash-attn --no-build-isolation
+```
+## Usage
+
+```python
+import torch
+from PIL import Image
+
+from colpali_engine.models import ColQwen2_5, ColQwen2_5_Processor
+
+model = ColQwen2_5.from_pretrained(
+        "tsystems/colqwen2.5-3b-multilingual-v1.0",
+        torch_dtype=torch.bfloat16,
+        device_map="cuda:0",  # or "mps" if on Apple Silicon
+    ).eval()
+processor = ColQwen2_5_Processor.from_pretrained("tsystems/colqwen2.5-3b-multilingual-v1.0")
+
+# 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
+
+ColQwen2.5's vision language backbone model (Qwen2.5-VL) is under `apache2.0` license. The adapters attached to the model are under MIT license.
+
+
+## Citation
+
+If you use this models from this organization in your research, please cite the original paper 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}, 
+}
+```
+- **Developed by:** [T-Systems International](https://www.t-systems.com/de/en)