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transformers/docs/source/en/model_doc/colpali.md
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transformers/docs/source/en/model_doc/colpali.md
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<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
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*This model was released on 2024-06-27 and added to Hugging Face Transformers on 2024-12-17.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# ColPali
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[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 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.
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This model was contributed by [@tonywu71](https://huggingface.co/tonywu71) (ILLUIN Technology) and [@yonigozlan](https://huggingface.co/yonigozlan) (HuggingFace).
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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.
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> [!TIP]
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> Click on the ColPali models in the right sidebar for more examples of how to use ColPali for image retrieval.
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<hfoptions id="usage">
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<hfoption id="image retrieval">
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```python
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import requests
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import torch
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from PIL import Image
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from transformers import ColPaliForRetrieval, ColPaliProcessor
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# Load the model and the processor
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model_name = "vidore/colpali-v1.3-hf"
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model = ColPaliForRetrieval.from_pretrained(
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model_name,
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dtype=torch.bfloat16,
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device_map="auto", # "cpu", "cuda", "xpu", or "mps" for Apple Silicon
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)
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processor = ColPaliProcessor.from_pretrained(model_name)
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# The document page screenshots from your corpus
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url1 = "https://upload.wikimedia.org/wikipedia/commons/8/89/US-original-Declaration-1776.jpg"
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url2 = "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Romeoandjuliet1597.jpg/500px-Romeoandjuliet1597.jpg"
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images = [
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Image.open(requests.get(url1, stream=True).raw),
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Image.open(requests.get(url2, stream=True).raw),
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]
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# The queries you want to retrieve documents for
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queries = [
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"When was the United States Declaration of Independence proclaimed?",
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"Who printed the edition of Romeo and Juliet?",
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]
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# Process the inputs
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inputs_images = processor(images=images).to(model.device)
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inputs_text = processor(text=queries).to(model.device)
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# Forward pass
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with torch.no_grad():
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image_embeddings = model(**inputs_images).embeddings
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query_embeddings = model(**inputs_text).embeddings
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# Score the queries against the images
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scores = processor.score_retrieval(query_embeddings, image_embeddings)
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print("Retrieval scores (query x image):")
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print(scores)
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```
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If you have issue with loading the images with PIL, you can use the following code to create dummy images:
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```python
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images = [
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Image.new("RGB", (128, 128), color="white"),
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Image.new("RGB", (64, 32), color="black"),
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]
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```
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</hfoption>
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</hfoptions>
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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.
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The example below uses [bitsandbytes](../quantization/bitsandbytes) to quantize the weights to int4.
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```python
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import requests
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import torch
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from PIL import Image
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from transformers import BitsAndBytesConfig, ColPaliForRetrieval, ColPaliProcessor
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model_name = "vidore/colpali-v1.3-hf"
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# 4-bit quantization configuration
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.float16,
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)
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model = ColPaliForRetrieval.from_pretrained(
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model_name,
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quantization_config=bnb_config,
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device_map="auto",
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)
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processor = ColPaliProcessor.from_pretrained(model_name)
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url1 = "https://upload.wikimedia.org/wikipedia/commons/8/89/US-original-Declaration-1776.jpg"
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url2 = "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Romeoandjuliet1597.jpg/500px-Romeoandjuliet1597.jpg"
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images = [
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Image.open(requests.get(url1, stream=True).raw),
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Image.open(requests.get(url2, stream=True).raw),
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]
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queries = [
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"When was the United States Declaration of Independence proclaimed?",
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"Who printed the edition of Romeo and Juliet?",
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]
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# Process the inputs
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inputs_images = processor(images=images, return_tensors="pt").to(model.device)
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inputs_text = processor(text=queries, return_tensors="pt").to(model.device)
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# Forward pass
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with torch.no_grad():
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image_embeddings = model(**inputs_images).embeddings
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query_embeddings = model(**inputs_text).embeddings
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# Score the queries against the images
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scores = processor.score_retrieval(query_embeddings, image_embeddings)
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print("Retrieval scores (query x image):")
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print(scores)
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```
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## Notes
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- [`~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.
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## ColPaliConfig
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[[autodoc]] ColPaliConfig
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## ColPaliProcessor
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[[autodoc]] ColPaliProcessor
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## ColPaliForRetrieval
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[[autodoc]] ColPaliForRetrieval
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- forward
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