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+*This model was published in HF papers on 2022-01-28 and contributed to Hugging Face Transformers on 2022-12-21.*
+
+
+# BLIP
+
+[BLIP](https://huggingface.co/papers/2201.12086) (Bootstrapped Language-Image Pretraining) is a vision-language pretraining (VLP) framework designed for *both* understanding and generation tasks. Most existing pretrained models are only good at one or the other. It uses a captioner to generate captions and a filter to remove the noisy captions. This increases training data quality and more effectively uses the messy web data.
+
+You can find all the original BLIP checkpoints under the [BLIP](https://huggingface.co/collections/Salesforce/blip-models-65242f40f1491fbf6a9e9472) collection.
+
+> [!TIP]
+> This model was contributed by [ybelkada](https://huggingface.co/ybelkada).
+>
+> Click on the BLIP models in the right sidebar for more examples of how to apply BLIP to different vision language tasks.
+
+The example below demonstrates how to visual question answering with [`Pipeline`] or the [`AutoModel`] class.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+pipeline = pipeline(
+    task="visual-question-answering",
+    model="Salesforce/blip-vqa-base",
+    device=0
+)
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+pipeline(question="What is the weather in this image?", image=url)
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+import requests
+import torch
+from PIL import Image
+
+from transformers import AutoModelForVisualQuestionAnswering, AutoProcessor
+
+
+processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base")
+model = AutoModelForVisualQuestionAnswering.from_pretrained(
+    "Salesforce/blip-vqa-base",
+    device_map="auto"
+)
+
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+question = "What is the weather in this image?"
+inputs = processor(images=image, text=question, return_tensors="pt").to(model.device, torch.float16)
+
+output = model.generate(**inputs)
+processor.batch_decode(output, skip_special_tokens=True)[0]
+```
+
+</hfoption>
+</hfoptions>
+
+## Resources
+
+Refer to this [notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb) to learn how to fine-tune BLIP for image captioning on a custom dataset.
+
+## BlipConfig
+
+[[autodoc]] BlipConfig
+
+## BlipTextConfig
+
+[[autodoc]] BlipTextConfig
+
+## BlipVisionConfig
+
+[[autodoc]] BlipVisionConfig
+
+## BlipProcessor
+
+[[autodoc]] BlipProcessor
+    - __call__
+
+## BlipImageProcessor
+
+[[autodoc]] BlipImageProcessor
+    - preprocess
+
+## BlipImageProcessorPil
+
+[[autodoc]] BlipImageProcessorPil
+    - preprocess
+
+## BlipModel
+
+`BlipModel` is going to be deprecated in future versions, please use `BlipForConditionalGeneration`, `BlipForImageTextRetrieval` or `BlipForQuestionAnswering` depending on your usecase.
+
+[[autodoc]] BlipModel
+    - forward
+    - get_text_features
+    - get_image_features
+
+## BlipTextModel
+
+[[autodoc]] BlipTextModel
+    - forward
+
+## BlipTextLMHeadModel
+
+[[autodoc]] BlipTextLMHeadModel
+    - forward
+
+## BlipVisionModel
+
+[[autodoc]] BlipVisionModel
+    - forward
+
+## BlipForConditionalGeneration
+
+[[autodoc]] BlipForConditionalGeneration
+    - forward
+
+## BlipForImageTextRetrieval
+
+[[autodoc]] BlipForImageTextRetrieval
+    - forward
+
+## BlipForQuestionAnswering
+
+[[autodoc]] BlipForQuestionAnswering
+    - forward