Create README.md

README.md

@@ -0,0 +1,43 @@
+---
+language: en
+license: mit
+tags:
+- vision
+- image-to-text
+pipeline_tag: image-to-text
+---
+
+# BLIP-2, OPT-2.7b, pre-trained only
+
+BLIP-2 model, leveraging [Flan T5-xl](https://huggingface.co/google/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](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).
+
+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.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg"
+alt="drawing" width="600"/> 
+
+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 previous conversation as prompt to the model
+
+## Intended uses & limitations
+
+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
+fine-tuned versions on a task that interests you.
+
+### How to use
+
+For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/model_doc/blip_2).