Create README.md

README.md

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+---
+license: mit
+datasets:
+- tattrongvu/vqa_de_en_batch1
+- vidore/colpali_train_set
+- tattrongvu/sharegpt4v_vqa_200k_batch1
+language:
+- en
+- de
+base_model:
+- Qwen/Qwen2-VL-7B-Instruct
+tags:
+- vidore
+- multimodal-embedding
+library_name: peft
+---
+# ColQwen2-7B: Visual Retriever based on Qwen2-VL-7B-Instruct with ColBERT strategy
+
+### This is the base version trained with batch_size 8x64 for 5 epoch and with the updated pad token
+
+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-VL-2B](https://huggingface.co/Qwen/Qwen2-VL-2B-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)
+
+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 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==0.3.4`.
+
+Data is the same as the ColPali data described in the paper.
+
+
+## Model Training
+
+### Dataset
+The dataset was extended from the original colpali train set with the gemini 1.5 flash generated QA on 35k images scraped from internet.
+
+*Note: Multilingual data is present in the pretraining corpus of the language model and most probably in the multimodal training.*
+
+### Parameters
+We train models  use low-rank adapters ([LoRA](https://arxiv.org/abs/2106.09685)) 
+with `alpha=64`  and `r=64` 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 64, in `bfloat16` format
+
+## Usage
+
+Make sure `colpali-engine` is installed from source or with a version superior to 0.3.4.
+`transformers` version must be > 4.46.1.
+
+```bash
+pip install git+https://github.com/illuin-tech/colpali
+```
+
+```python
+import torch
+from PIL import Image
+
+from colpali_engine.models import ColQwen2, ColQwen2Processor
+
+model = ColQwen2.from_pretrained(
+        "tsystems/colqwen2-7b-v1.0",
+        torch_dtype=torch.bfloat16,
+        device_map="cuda:0",  # or "mps" if on Apple Silicon
+    ).eval()
+processor = ColQwen2Processor.from_pretrained("tsystems/colqwen2-7b-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's vision language backbone model (Qwen2-VL) is under `apache2.0` license. The adapters attached to the model are under MIT license.