transformers/docs/source/en/model_doc/colqwen2.md

This model was published in HF papers on 2024-06-27 and contributed to Hugging Face Transformers on 2025-06-02.

ColQwen2

ColQwen2 is a variant of the 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 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 (ILLUIN Technology) and @yonigozlan (HuggingFace).

You can find all the original ColPali checkpoints under Vidore's Hf-native ColVision Models collection.

Tip

Click on the ColQwen2 models in the right sidebar for more examples of how to use ColQwen2 for image retrieval.

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:

images = [
    Image.new("RGB", (128, 128), color="white"),
    Image.new("RGB", (64, 32), color="black"),
]

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses bitsandbytes to quantize the weights to int4.

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 as the backbone.

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