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+*This model was published in HF papers on 2024-06-27 and contributed to Hugging Face Transformers on 2025-06-02.*
+
+
+# ColQwen2
+
+[ColQwen2](https://huggingface.co/papers/2407.01449) is a variant of the [ColPali](./colpali) model designed to retrieve documents by analyzing their visual features. Unlike traditional systems that rely heavily on text extraction and OCR, ColQwen2 treats each page as an image. It uses the [Qwen2-VL](./qwen2_vl) backbone to capture not only text, but also the layout, tables, charts, and other visual elements to create detailed multi-vector embeddings that can be used for retrieval by computing pairwise late interaction similarity scores. This offers a more comprehensive understanding of documents and enables more efficient and accurate retrieval.
+
+This model was contributed by [@tonywu71](https://huggingface.co/tonywu71) (ILLUIN Technology) and [@yonigozlan](https://huggingface.co/yonigozlan) (HuggingFace).
+
+You can find all the original ColPali checkpoints under Vidore's [Hf-native ColVision Models](https://huggingface.co/collections/vidore/hf-native-colvision-models-6755d68fc60a8553acaa96f7) collection.
+
+> [!TIP]
+> Click on the ColQwen2 models in the right sidebar for more examples of how to use ColQwen2 for image retrieval.
+
+<hfoptions id="usage">
+<hfoption id="image retrieval">
+
+```python
+import requests
+import torch
+from PIL import Image
+
+from transformers import ColQwen2ForRetrieval, ColQwen2Processor
+from transformers.utils.import_utils import is_flash_attn_2_available
+
+
+# Load the model and the processor
+model_name = "vidore/colqwen2-v1.0-hf"
+
+model = ColQwen2ForRetrieval.from_pretrained(
+    model_name,
+    device_map="auto",  # "cpu", "cuda", "xpu" or "mps" for Apple Silicon
+    attn_implementation="flash_attention_2" if is_flash_attn_2_available() else "sdpa",
+)
+processor = ColQwen2Processor.from_pretrained(model_name)
+
+# The document page screenshots from your corpus
+url1 = "https://upload.wikimedia.org/wikipedia/commons/8/89/US-original-Declaration-1776.jpg"
+url2 = "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Romeoandjuliet1597.jpg/500px-Romeoandjuliet1597.jpg"
+
+images = [
+    Image.open(requests.get(url1, stream=True).raw),
+    Image.open(requests.get(url2, stream=True).raw),
+]
+
+# The queries you want to retrieve documents for
+queries = [
+    "When was the United States Declaration of Independence proclaimed?",
+    "Who printed the edition of Romeo and Juliet?",
+]
+
+# Process the inputs
+inputs_images = processor(images=images).to(model.device)
+inputs_text = processor(text=queries).to(model.device)
+
+# Forward pass
+with torch.no_grad():
+    image_embeddings = model(**inputs_images).embeddings
+    query_embeddings = model(**inputs_text).embeddings
+
+# Score the queries against the images
+scores = processor.score_retrieval(query_embeddings, image_embeddings)
+
+print("Retrieval scores (query x image):")
+print(scores)
+```
+
+If you have issue with loading the images with PIL, you can use the following code to create dummy images:
+
+```python
+images = [
+    Image.new("RGB", (128, 128), color="white"),
+    Image.new("RGB", (64, 32), color="black"),
+]
+```
+
+</hfoption>
+</hfoptions>
+
+Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
+
+The example below uses [bitsandbytes](../quantization/bitsandbytes) to quantize the weights to int4.
+
+```python
+import requests
+import torch
+from PIL import Image
+
+from transformers import BitsAndBytesConfig, ColQwen2ForRetrieval, ColQwen2Processor
+
+
+model_name = "vidore/colqwen2-v1.0-hf"
+
+# 4-bit quantization configuration
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+)
+
+model = ColQwen2ForRetrieval.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map="auto",
+).eval()
+
+processor = ColQwen2Processor.from_pretrained(model_name)
+
+url1 = "https://upload.wikimedia.org/wikipedia/commons/8/89/US-original-Declaration-1776.jpg"
+url2 = "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Romeoandjuliet1597.jpg/500px-Romeoandjuliet1597.jpg"
+
+images = [
+    Image.open(requests.get(url1, stream=True).raw),
+    Image.open(requests.get(url2, stream=True).raw),
+]
+
+queries = [
+    "When was the United States Declaration of Independence proclaimed?",
+    "Who printed the edition of Romeo and Juliet?",
+]
+
+# Process the inputs
+inputs_images = processor(images=images, return_tensors="pt").to(model.device)
+inputs_text = processor(text=queries, return_tensors="pt").to(model.device)
+
+# Forward pass
+with torch.no_grad():
+    image_embeddings = model(**inputs_images).embeddings
+    query_embeddings = model(**inputs_text).embeddings
+
+# Score the queries against the images
+scores = processor.score_retrieval(query_embeddings, image_embeddings)
+
+print("Retrieval scores (query x image):")
+print(scores)
+```
+
+You can also use checkpoints for `ColQwen2.5` that are **compatible with the ColQwen2 architecture**. This version of the model uses [Qwen2_5_VL](./qwen2_5_vl) as the backbone.
+
+```python
+from transformers import ColQwen2ForRetrieval, ColQwen2Processor
+from transformers.utils.import_utils import is_flash_attn_2_available
+
+
+model_name = "Sahil-Kabir/colqwen2.5-v0.2-hf" # An existing compatible checkpoint
+
+model = ColQwen2ForRetrieval.from_pretrained(
+    model_name,
+    device_map="auto",
+    attn_implementation="flash_attention_2" if is_flash_attn_2_available() else "sdpa"
+)
+processor = ColQwen2Processor.from_pretrained(model_name)
+```
+
+## Notes
+
+- [`~ColQwen2Processor.score_retrieval`] returns a 2D tensor where the first dimension is the number of queries and the second dimension is the number of images. A higher score indicates more similarity between the query and image.
+- Unlike ColPali, ColQwen2 supports arbitrary image resolutions and aspect ratios, which means images are not resized into fixed-size squares. This preserves more of the original input signal.
+- Larger input images generate longer multi-vector embeddings, allowing users to adjust image resolution to balance performance and memory usage.
+
+## ColQwen2Config
+
+[[autodoc]] ColQwen2Config
+
+## ColQwen2Processor
+
+[[autodoc]] ColQwen2Processor
+    - __call__
+
+## ColQwen2ForRetrieval
+
+[[autodoc]] ColQwen2ForRetrieval
+    - forward