Metric-AI
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ColQwenStella-2b-multilingual

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 ---
-# base_model: ./models/qwenstella-base
 library_name: peft
 ---
-# Model Card for Model ID
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-## Model Details
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-### Framework versions
-- PEFT 0.11.1

 ---
+license: cc-by-nc-4.0
+datasets:
+- openbmb/VisRAG-Ret-Train-Synthetic-data
+- openbmb/VisRAG-Ret-Train-In-domain-data
+- Metric-AI/rag_docmatix_100k
+- vidore/colpali_train_set
+- llamaindex/vdr-multilingual-train
+- Metric-AI/tabfquad_train_set
+language:
+- en
+- fr
+- es
+- it
+- de
+base_model:
+- Metric-AI/ColQwenStella-base-2b
+- Qwen/Qwen2-VL-2B
+- NovaSearch/stella_en_1.5B_v5
+tags:
+- vidore
+- multimodal_embedding
+- multilingual_embedding
+- Text-to-Visual Document (T→VD) retrieval
 library_name: peft
+pipeline_tag: visual-document-retrieval
 ---
+# ColQwenStella-2b-multilingual: Multilingual Visual Retriever based on the combination of Qwen2 Vision and stella_en_1.5B_v5 model.
+## Ranked #1 among models <= 2B parameters and #5 overall on the Vidore benchmark (as of February 11, 2025). The reported scores on the [Vidore Leaderboard](https://huggingface.co/spaces/vidore/vidore-leaderboard) correspond to checkpoint-1800.
+### This is the base version trained on 4xA100 80GB with per_device_batch_size=128 for 5 epoch.
+The ColQwenStella-2b-multilingual architecture combines the Vision component of the Qwen2 model with stella_en_1.5B_v5 as its embedding model. Training is done following the [ColPali: Efficient Document Retrieval with Vision Language Models](https://arxiv.org/abs/2407.01449) recipe.
+## Data
+- **Synthetic data**: Selected and preprocessed from the `openbmb/VisRAG-Ret-Train-Synthetic-data` dataset.
+- **In-domain VQA dataset**: Drawn from `openbmb/VisRAG-Ret-Train-In-domain-data`.
+- **Docmatix dataset**: Extracted from the `Metric-AI/rag_docmatix_100k` dataset.
+- **Colpali dataset**: Taken from `vidore/colpali_train_set`.
+- **Multilingual dataset**: Taken from `llamaindex/vdr-multilingual-train`.
+## 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, and `mlp` layers of the `vison_model.merger`
+as well as the final randomly initialized projection layer, and use a `adamw` optimizer.
+We train on an 4xA100 GPU setup with distributed data parallelism (via accelerate), a learning rate of 5e-4 with cosine decay with 100 warmup steps, batch size per device is 128, in `bfloat16` format.
+## Installation
+```bash
+pip install transformers>=4.46.3
+```
+## Usage
+```python
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoProcessor
+model = AutoModel.from_pretrained(
+        "Metric-AI/ColQwenStella-2b-multilingual",
+        torch_dtype=torch.bfloat16,
+        device_map="cuda:0",  # or "mps" if on Apple Silicon
+        trust_remote_code=True
+    ).eval()
+processor = AutoProcessor.from_pretrained("Metric-AI/ColQwenStella-2b-multilingual", trust_remote_code=True)
+# 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)
+```
+## License
+The adapters attached to the model are under MIT license.
+- **Developed by:** [Metric AI Research Lab](https://metric.am/)