Update README.md

README.md

@@ -11,4 +11,38 @@ base_model:
 tags:
 - vidore
 - multimodal_embedding
----
+---
+# ColQwen2-2B: Visual Retriever based on Qwen2-VL-7B-Instruct with ColBERT strategy
+
+### This is the base version trained with batch_size 8x128 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 1024 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=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