MobileVLM : A Fast, Reproducible and Strong Vision Language Assistant for Mobile Devices
Paper • 2312.16886 • Published • 22
How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "mtgv/MobileLLaMA-1.4B-Chat" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "mtgv/MobileLLaMA-1.4B-Chat",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
Model Summery
MobileLLaMA-1.4B-Chat is fine-tuned from MobileLLaMA-1.4B-Base with supervised instruction fine-tuning on ShareGPT dataset.
Model Sources
- Repository: https://github.com/Meituan-AutoML/MobileVLM
- Paper: https://arxiv.org/abs/2312.16886
How to Get Started with the Model
Model weights can be loaded with Hugging Face Transformers. Examples can be found at Github.
Training Details
please refer to our paper in section 4.1: MobileVLM: A Fast, Strong and Open Vision Language Assistant for Mobile Devices.
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "mtgv/MobileLLaMA-1.4B-Chat" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mtgv/MobileLLaMA-1.4B-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'