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Update README.md

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  ---
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  title: VLM Demo
 
 
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  app_file: app.py
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  ---
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@@ -23,7 +25,7 @@ This repository also requires that the `vlm-bench` package (`vlbench`) and
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  `prismatic-vlms` package (`prisma`) are installed in the current environment.
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  These can both be installed from source from the following git repos:
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- + `vlm-bench`: `https://github.com/TRI-ML/vlm-bench`
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  + `prismatic-vlms`: `https://github.com/TRI-ML/prismatic-vlms`
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  ## Usage
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  The default interaction mode is Chat, which is the main way to use our models. However, we also support a number of other
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  interaction modes for more specific use cases:
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- + Captioning: Here, you can simply upload an image with no provided prompt and the selected model will output a caption. Even if a prompt
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  is input by the user, it will not be used in producing the caption.
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  + Bounding Box Prediction: After uploading an image, simply specify a portion of the image for which bounding box coordinates are desired
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  in the prompt and the selected model will output corresponding coordinates.
 
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  ---
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  title: VLM Demo
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+ sdk: gradio
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+ sdk_version: 3.35.2
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  app_file: app.py
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  ---
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  `prismatic-vlms` package (`prisma`) are installed in the current environment.
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  These can both be installed from source from the following git repos:
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+ + `vlm-bench`: `htts://github.com/TRI-ML/vlm-bench`
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  + `prismatic-vlms`: `https://github.com/TRI-ML/prismatic-vlms`
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  ## Usage
 
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  The default interaction mode is Chat, which is the main way to use our models. However, we also support a number of other
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  interaction modes for more specific use cases:
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+ + Captioning: Here,you can simply upload an image with no provided prompt and the selected model will output a caption. Even if a prompt
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  is input by the user, it will not be used in producing the caption.
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  + Bounding Box Prediction: After uploading an image, simply specify a portion of the image for which bounding box coordinates are desired
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  in the prompt and the selected model will output corresponding coordinates.