readme cleanup

README.md

@@ -7,97 +7,10 @@ license: 	mit
 This demo illustrates the work published in the paper ["Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models"](https://arxiv.org/pdf/2402.07865.pdf)
 
 
-# VLM Demo
+# Source code
+
+For more information, please refer to this repository:
 
 > *VLM Demo*: Lightweight repo for chatting with VLMs supported by our 
 [VLM Evaluation Suite](https://github.com/TRI-ML/vlm-evaluation/tree/main).
 
----
-
-## Installation
-
-This repository can be installed as follows:
-
-```bash
-git clone [email protected]:TRI-ML/vlm-demo.git
-cd vlm-demo
-pip install -e .
-```
-
-This repository also requires that the `vlm-evaluation` package (`vlm_eval`) is
-installed in the current environment. Installation instructions can be found
-[here](https://github.com/TRI-ML/vlm-evaluation/tree/main).
-
-## Usage
-
-The main script to run is `interactive_demo.py`, while the implementation of 
-the Gradio Controller (`serve/gradio_controller.py`) and Gradio Web Server
-(`serve/gradio_web_server.py`) are within `serve`. All of this code is heavily
-adapted from the [LLaVA Github Repo](https://github.com/haotian-liu/LLaVA/blob/main/llava/serve/).
-More details on how this code was modified from the original LLaVA repo is provided in the 
-relevant source files.
-
-To run the demo, first run the following commands in separate terminals:
-
-+ Start Gradio Controller: `python -m serve.controller --host 0.0.0.0 --port 10000`
-+ Start Gradio Web Server: `python -m serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload --share`
-
-To run the interactive demo, you can specify a model to chat with via a `model_dir` or `model_id` as follows
-
-+ `python -m interactive_demo  --port 40000  --model_id <MODEL_ID>` OR
-+ `python -m interactive_demo  --port 40000  --model_dir <MODEL_DIR>`
-
-If you want to chat with multiple models simultaneously, you can launch the `interactive_demo` script in different terminals.
-
-When running the demo, the following parameters are adjustable:
-+ Temperature
-+ Max output tokens
-
-The default interaction mode is Chat, which is the main way to use our models. However, we also support a number of other 
-interaction modes for more specific use cases:
-+ Captioning: Here,you can simply upload an image with no provided prompt and the selected model will output a caption. Even if a prompt
-is input by the user, it will not be used in producing the caption.
-+ Bounding Box Prediction: After uploading an image, simply specify a portion of the image for which bounding box coordinates are desired
-in the prompt and the selected model will output corresponding coordinates.
-+ Visual Question Answering: Selecting this option is best when the user wants short, succint answers to a specific question provided in the
-prompt.
-+ True/False Question Answering: Selecting this option is best when the user wants a True/False answer to a specific question provided in the 
-prompt.
-
-## Example
-
-To chat with the LLaVa 1.5 (7B) and Prism 7B models in an interactive GUI, run the following scripts in separate terminals.
-
-Launch gradio controller: 
-
-`python -m serve.controller --host 0.0.0.0 --port 10000`
-
-Launch web server: 
-
-`python -m serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload --share`
-
-Now we can launch an interactive demo corresponding to each of the models we want to chat with. For Prism models, you
-onl need to specify a `model_id`, while for LLaVA and InstructBLIP, you need to additionally specifiy a `model_family`
-and `model_dir`. Note that for each model, a different port must be specified.
-
-Launch interactive demo for Prism 7B Model: 
-
-`python -m interactive_demo --port 40000 --model_id prism-dinosiglip+7b`
-
-Launch interactive demo for LLaVA 1.5 7B Model: 
-
-`python -m interactive_demo --port 40001 --model_family llava-v15 --model_id llava-v1.5-7b --model_dir liuhaotian/llava-v1.5-7b`
-
-## Contributing
-
-Before committing to the repository, *make sure to set up your dev environment!*
-
-Here are the basic development environment setup guidelines:
-
-+ Fork/clone the repository, performing an editable installation. Make sure to install with the development dependencies
-  (e.g., `pip install -e ".[dev]"`); this will install `black`, `ruff`, and `pre-commit`.
-
-+ Install `pre-commit` hooks (`pre-commit install`).
-
-+ Branch for the specific feature/issue, issuing PR against the upstream repository for review.
-