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+ ---
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+ language: en
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+ license: mit
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+ tags:
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+ - vision
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+ - image-to-text
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+ pipeline_tag: image-to-text
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+ ---
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+
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+ # BLIP-2, OPT-2.7b, pre-trained only
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+
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+ BLIP-2 model, leveraging [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language model with 2.7 billion parameters).
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+ It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2).
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+
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+ Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team.
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+
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+ ## Model description
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+
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+ BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model.
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+
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+ The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen
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+ while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings,
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+ which bridge the gap between the embedding space of the image encoder and the large language model.
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+
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+ The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text.
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+
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+ <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg"
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+ alt="drawing" width="600"/>
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+
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+ This allows the model to be used for tasks like:
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+
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+ - image captioning
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+ - visual question answering (VQA)
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+ - chat-like conversations by feeding the image and the previous conversation as prompt to the model
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+
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+ ## Intended uses & limitations
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+
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+ You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for
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+ fine-tuned versions on a task that interests you.
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+
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+ ### How to use
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+
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+ For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/model_doc/blip_2).