Update README.md

README.md

@@ -47,4 +47,74 @@ The dataset was extended from the original colpali train set with the gemini 1.5
 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 distriuted 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
+We train on an 8xH100 GPU setup with distriuted 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(
+        "vidore/colqwen2-v1.0",
+        torch_dtype=torch.bfloat16,
+        device_map="cuda:0",  # or "mps" if on Apple Silicon
+    ).eval()
+processor = ColQwen2Processor.from_pretrained("vidore/colqwen2-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.
+
+## 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}, 
+}
+```