43 lines
2.1 KiB
Markdown
43 lines
2.1 KiB
Markdown
---
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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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# BLIP-2, OPT-6.7b, fine-tuned on COCO
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BLIP-2 model, leveraging [OPT-6.7b](https://huggingface.co/facebook/opt-6.7b) (a large language model with 6.7 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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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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## Model description
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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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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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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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<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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This allows the model to be used for tasks like:
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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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## Intended uses & limitations
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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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### How to use
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For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example). |