---
license: apache-2.0
---
# Chinese Visual-language Multi-modal models for captions and robot actions

## Release
- [9/22] 🔥 We release two major models. The *CN-caption* model is for accurate chinese image captioning while *robot action* model is for demo-level robot action.

## Contents
### CNCaption models
This model can provide accurate and fine-grained Chinese descriptions of given images.


### robot models
This model can provide accurate instructions for robot actions

## Install

1. Install Package
```Shell
conda create -n llava python=3.10 -y
conda activate llava
pip install --upgrade pip  
pip install -e .
```

2. Install additional packages for training cases
```
pip install ninja
pip install flash-attn --no-build-isolation
```

## Demo

To run our demo, you need to prepare LLaVA checkpoints locally.  Please follow the instructions [here](#llava-weights) to download the checkpoints.

### Gradio Web UI

To launch a Gradio demo locally, please run the following commands one by one. If you plan to launch multiple model workers to compare between different checkpoints, you only need to launch the controller and the web server *ONCE*.

#### Launch a controller
```Shell
python -m llava.serve.controller --host 0.0.0.0 --port 10000
```

#### Launch a gradio web server.
```Shell
python -m llava.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload
```
You just launched the Gradio web interface. Now, you can open the web interface with the URL printed on the screen worker.

#### Launch a model worker

This is the actual *worker* that performs the inference on the GPU.  Each worker is responsible for a single model specified in `--model-path`.

```Shell
python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path {--model-path}
```

## API inference
We also provide an API interface for more convenient use. We provide server-side startup scripts and client-side test code here.


### Server
```Shell
python -m llava.serve.controller --host 0.0.0.0 --port 10000
python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path {--model-path}
```

### Client

```Shell
python req_test.py ${text} ${image}
```