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FUXI/Multi-modal_10B_CN
Multi-modal_10B_CN is a machine learning model from FUXI. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
- [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.
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Updated Sep 27, 2023
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From the Hugging Face model README
This model can provide accurate and fine-grained Chinese descriptions of given images.
This model can provide accurate instructions for robot actions
conda create -n llava python=3.10 -y
conda activate llava
pip install --upgrade pip
pip install -e .
pip install ninja
pip install flash-attn --no-build-isolation
To run our demo, you need to prepare LLaVA checkpoints locally. Please follow the instructions here to download the checkpoints.
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.
python -m llava.serve.controller --host 0.0.0.0 --port 10000
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.
This is the actual worker that performs the inference on the GPU. Each worker is responsible for a single model specified in --model-path.
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}
We also provide an API interface for more convenient use. We provide server-side startup scripts and client-side test code here.
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}
python req_test.py ${text} ${image}