BLIP 2, Flan T5 xl, pre trained only BLIP 2 model, leveraging Flan T5 xl (a large language model). It was introduced in the paper BLIP 2: Bootstrapping Language Image Pre training with Frozen Image Encoders and Large Language Models by Li et al. and first released in this repository. 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. Model description BLIP 2 consists of 3 models: a CLIP like image encoder, a Querying Transformer (Q Former) and a large language model. The authors initialize the weights of the image encoder and large language model from pre trained checkpoints and keep them frozen while training the Querying Transformer, which is a BERT like Transformer encoder that maps a set of "query tokens" to query embeddings, which bridge the gap between the embedding space of the image encoder and the large language model. The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text. This allows the model to be used for tasks like: image captioning visual question answering (VQA) chat like conversations by feeding the image and the pr…
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