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

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# ColPali

[ColPali](https://huggingface.co/papers/2407.01449) 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](./paligemma) 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](https://huggingface.co/collections/vidore/hf-native-colvision-models-6755d68fc60a8553acaa96f7) collection.

> [!TIP]
> Click on the ColPali models in the right sidebar for more examples of how to use ColPali for image retrieval.

<hfoptions id="usage">
<hfoption id="image retrieval">

```py
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)
```
</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.md) to quantize the weights to int4.

```py
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