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

ColPali

ColPali is a model designed to retrieve documents by analyzing their visual features. Unlike traditional systems that rely heavily on text extraction and OCR, ColPali treats each page as an image. It uses Paligemma-3B to capture not only text, but also the layout, tables, charts, and other visual elements to create detailed embeddings. This offers a more comprehensive understanding of documents and enables more efficient and accurate retrieval.

You can find all the original ColPali checkpoints under the ColPali collection.

Tip

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

import requests
import torch
from PIL import Image
from transformers import ColPaliForRetrieval, ColPaliProcessor

# Load model (bfloat16 support is limited; fallback to float32 if needed)
model = ColPaliForRetrieval.from_pretrained(
    "vidore/colpali-v1.2-hf",
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
    device_map="auto",  # "cpu", "cuda", or "mps" for Apple Silicon
).eval()

processor = ColPaliProcessor.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 = [
    "Who printed the edition of Romeo and Juliet?",
    "When was the United States Declaration of Independence proclaimed?",
]

# 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

scores = processor.score_retrieval(query_embeddings, image_embeddings)

print("Retrieval scores (query x image):")
print(scores)

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 ColPaliForRetrieval, ColPaliProcessor
from transformers import BitsAndBytesConfig

# 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_name = "vidore/colpali-v1.2-hf"

# Load model 
model = ColPaliForRetrieval.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="cuda"
).eval()

processor = ColPaliProcessor.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 = [
    "Who printed the edition of Romeo and Juliet?",
    "When was the United States Declaration of Independence proclaimed?",
]

# 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

scores = processor.score_retrieval(query_embeddings, image_embeddings)

print("Retrieval scores (query x image):")
print(scores)

Notes

• [~ColPaliProcessor.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.

ColPaliConfig

autodoc ColPaliConfig

ColPaliProcessor

autodoc ColPaliProcessor

ColPaliForRetrieval

autodoc ColPaliForRetrieval - forward