Create README.md

README.md

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+---
+base_model:
+- vidore/colqwen2.5omni-base
+license: mit
+library_name: colpali
+language:
+- en
+tags:
+- colpali
+- vidore
+- vidore-experimental
+pipeline_tag: visual-document-retrieval
+---
+
+# ColQwen2.5-Omni: Visual+Audio Retriever based on Qwen2.5-Omni-3B-Instruct with ColBERT strategy
+
+Check out the release [blogpost](https://huggingface.co/blog/manu/colqwen-omni-omnimodal-retrieval) for in-depth explanations and tutorials!
+
+ColQwen-Omni is a model based on a novel model architecture and training strategy based on Omnimodal Language Models to efficiently index documents from their visual features.
+It is a Qwen2.5-Omni-3B 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 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==0.3.11`.
+
+Data is the same as the ColPali data described in the paper.
+
+
+## Model Training
+
+### Dataset
+
+The audio retrieval capabilities are acquired in a 0-shot capacity, as the entire training data is purely image-text matching. Yhe audio and vision tower are frozen during training.
+
+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.*
+
+## Usage
+
+Make sure `colpali-engine` is installed from source or with a version superior to 0.3.11.
+
+```bash
+pip install git+https://github.com/illuin-tech/colpali
+```
+
+```python
+
+import torch
+from PIL import Image
+from transformers.utils.import_utils import is_flash_attn_2_available
+from tqdm import tqdm
+from torch.utils.data import DataLoader
+
+from colpali_engine.models import ColQwen2_5Omni, ColQwen2_5OmniProcessor
+
+model = ColQwen2_5Omni.from_pretrained(
+    "vidore/colqwen-omni-v0.1",
+    torch_dtype=torch.bfloat16,
+    device_map="cuda",  # or "mps" if on Apple Silicon
+    attn_implementation="flash_attention_2" # if is_flash_attn_2_available() else None,
+).eval()
+processor = ColQwen2_5OmniProcessor.from_pretrained("vidore/colqwen-omni-v0.1")
+
+dataset = load_dataset("eustlb/dailytalk-conversations-grouped", split="train[:500]")
+audios = [x["array"] for x in dataset["audio"]]
+
+
+dataloader = DataLoader(
+    dataset=audios,
+    batch_size=2,
+    shuffle=False,
+    collate_fn=lambda x: processor.process_audios(x),
+)
+
+ds  = []
+for batch_doc in tqdm(dataloader):
+    with torch.no_grad():
+        batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}
+        embeddings_doc = model(**batch_doc)
+    ds.extend(list(torch.unbind(embeddings_doc.to("cpu"))))
+
+def get_results(query: str, k=10):
+    batch_queries = processor.process_queries([query]).to(model.device)
+
+    # Forward pass
+    with torch.no_grad():
+        query_embeddings = model(**batch_queries)
+
+    scores = processor.score_multi_vector(query_embeddings, ds)
+    # get top-5 scores
+    return scores[0].topk(k).indices.tolist()
+
+res = get_results("A person looking for a taxi")
+
+# In colab
+display(Audio(dataset[res[0]]["audio"]["array"], autoplay=True, rate=dataset[res[0]]["audio"]["sampling_rate"]))
+```
+
+## Contact
+
+- Manuel Faysse: manuel.faysse@illuin.tech
+- Antonio Loison: antonio.loison@illuin.tech
+
+## 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}, 
+}
+```