vidore
/

colqwen2.5-v0.2

@@ -1,202 +1,122 @@
 ---
 base_model: vidore/colqwen2.5-base
-library_name: peft
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.14.0

 ---
+license: mit
+library_name: colpali
 base_model: vidore/colqwen2.5-base
+language:
+- en
+tags:
+- colpali
+- vidore
+- vidore-experimental
 ---
+# ColQwen2.5: Visual Retriever based on Qwen2.5-VL-3B-Instruct with ColBERT strategy
+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==0.3.7`.
+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
+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
+Make sure `colpali-engine` is installed from source or with a version superior to 0.3.1.
+`transformers` version must be > 4.45.0.
+```bash
+pip install git+https://github.com/illuin-tech/colpali
+```
+```python
+import torch
+from PIL import Image
+from colpali_engine.models import ColQwen2_5, ColQwen2_5_Processor
+model = ColQwen2_(.from_pretrained(
+        "vidore/colqwen2.5-v0.1",
+        torch_dtype=torch.bfloat16,
+        device_map="cuda:0",  # or "mps" if on Apple Silicon
+    ).eval()
+processor = ColQwen2_5_Processor.from_pretrained("vidore/colqwen2.5-v0.1")
+# 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.
+## 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},
+}
+```