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+*This model was published in HF papers on 2022-11-12 and contributed to Hugging Face Transformers on 2023-01-04.*
+
+# AltCLIP
+
+[AltCLIP](https://huggingface.co/papers/2211.06679) replaces the [CLIP](./clip) text encoder with a multilingual XLM-R encoder and aligns image and text representations with teacher learning and contrastive learning.
+
+You can find all the original AltCLIP checkpoints under the [AltClip](https://huggingface.co/collections/BAAI/alt-clip-diffusion-66987a97de8525205f1221bf) collection.
+
+> [!TIP]
+> Click on the AltCLIP models in the right sidebar for more examples of how to apply AltCLIP to different tasks.
+
+The examples below demonstrates how to calculate similarity scores between an image and one or more captions with the [`AutoModel`] class.
+
+<hfoptions id="usage">
+<hfoption id="AutoModel">
+
+```python
+import requests
+from PIL import Image
+
+from transformers import AltCLIPModel, AltCLIPProcessor
+
+
+model = AltCLIPModel.from_pretrained("BAAI/AltCLIP", device_map="auto")
+processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
+
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True).to(model.device)
+
+outputs = model(**inputs)
+logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
+probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
+
+labels = ["a photo of a cat", "a photo of a dog"]
+for label, prob in zip(labels, probs[0]):
+    print(f"{label}: {prob.item():.4f}")
+```
+
+</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 [torchao](../quantization/torchao) to only quantize the weights to int4.
+
+```python
+# !pip install torchao
+import requests
+from PIL import Image
+
+from transformers import AltCLIPModel, AltCLIPProcessor, TorchAoConfig
+
+
+model = AltCLIPModel.from_pretrained(
+    "BAAI/AltCLIP",
+    quantization_config=TorchAoConfig("int4_weight_only", group_size=128),
+    device_map="auto",
+)
+
+processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
+
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True).to(model.device)
+
+outputs = model(**inputs)
+logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
+probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
+
+labels = ["a photo of a cat", "a photo of a dog"]
+for label, prob in zip(labels, probs[0]):
+    print(f"{label}: {prob.item():.4f}")
+```
+
+## Notes
+
+- AltCLIP uses bidirectional attention instead of causal attention and it uses the `[CLS]` token in XLM-R to represent a text embedding.
+- Use [`CLIPImageProcessor`] to resize (or rescale) and normalize images for the model.
+- [`AltCLIPProcessor`] combines [`CLIPImageProcessor`] and [`XLMRobertaTokenizer`] into a single instance to encode text and prepare images.
+
+## AltCLIPConfig
+
+[[autodoc]] AltCLIPConfig
+
+## AltCLIPTextConfig
+
+[[autodoc]] AltCLIPTextConfig
+
+## AltCLIPVisionConfig
+
+[[autodoc]] AltCLIPVisionConfig
+
+## AltCLIPModel
+
+[[autodoc]] AltCLIPModel
+
+## AltCLIPTextModel
+
+[[autodoc]] AltCLIPTextModel
+
+## AltCLIPVisionModel
+
+[[autodoc]] AltCLIPVisionModel
+
+## AltCLIPProcessor
+
+[[autodoc]] AltCLIPProcessor
+    - __call__