Instructions to use mtgv/MobileLLaMA-2.7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mtgv/MobileLLaMA-2.7B-Chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mtgv/MobileLLaMA-2.7B-Chat")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("mtgv/MobileLLaMA-2.7B-Chat")
model = AutoModelForMultimodalLM.from_pretrained("mtgv/MobileLLaMA-2.7B-Chat")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mtgv/MobileLLaMA-2.7B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mtgv/MobileLLaMA-2.7B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mtgv/MobileLLaMA-2.7B-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mtgv/MobileLLaMA-2.7B-Chat

SGLang

How to use mtgv/MobileLLaMA-2.7B-Chat with SGLang:

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-2.7B-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-2.7B-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use 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-2.7B-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-2.7B-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mtgv/MobileLLaMA-2.7B-Chat with Docker Model Runner:
```
docker model run hf.co/mtgv/MobileLLaMA-2.7B-Chat
```

Model Summery

MobileLLaMA-2.7B-Chat is fine-tuned from MobileLLaMA-2.7B-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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Dataset used to train mtgv/MobileLLaMA-2.7B-Chat

Paper for mtgv/MobileLLaMA-2.7B-Chat

MobileVLM : A Fast, Reproducible and Strong Vision Language Assistant for Mobile Devices

Paper • 2312.16886 • Published Dec 28, 2023 • 22