blip-3
Maintainer: zsxkib
894
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | No paper link provided |
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Model overview
blip-3 is a series of large multimodal models (LMMs) developed by Salesforce AI Research. These models have been trained at scale on high-quality image caption datasets and interleaved image-text data. blip3-phi3-mini-instruct-r-v1 is a fine-tuned version of the pretrained blip3-phi3-mini-base-r-v1 model that achieves state-of-the-art performance among open-source and closed-source vision-language models under 5 billion parameters. It supports flexible high-resolution image encoding with efficient visual token sampling.
The blip-3 model series is related to other multimodal models like SDXL-Lightning from ByteDance, which generates high-quality images in 4 steps, and the original BLIP model from Salesforce, which generates image captions. The BLIP-2 model from Andreas Jansson also answers questions about images.
Model inputs and outputs
Inputs
- Image: The input image to generate captions or answer questions about.
- Question: The question to ask about the input image.
- Context (optional): Previous questions and answers to use as context for answering the current question.
- Miscellaneous parameters: Options to control the output, such as the number of top tokens to consider, the temperature for sampling, and whether to use beam search.
Outputs
- String: The model's response to the input question, either a caption or an answer.
Capabilities
The blip-3 models excel at answering questions about images, with state-of-the-art performance on benchmarks like COCO, NoCaps, TextCaps, OKVQA, TextVQA, VizWiz, and VQAv2. They can provide detailed, polite, and helpful answers to a wide variety of image-related questions.
What can I use it for?
The blip-3 models can be useful for building applications that need to understand and reason about images, such as:
- Visual question answering systems
- Image captioning tools
- Multimodal search engines
- Automated image analysis for e-commerce or other domains
The maintainer's profile also showcases their work on the related uform-gen model, which is a fast 1.5B image captioning and VQA multimodal language model.
Things to try
One interesting aspect of the blip-3 models is their ability to perform in-context learning, where they can leverage previous questions and answers to provide more contextual responses. You could experiment with different ways of providing context to the model and see how it affects the quality and relevance of the answers.
Another area to explore is the model's performance on specialized tasks like document understanding, chart analysis, or OCR-related questions. The README mentions the model was trained on a mixture of academic VQA datasets covering these types of tasks, so it could be worth testing its capabilities in these domains.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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