Instructions to use Efficient-Large-Model/VILA-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Efficient-Large-Model/VILA-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Efficient-Large-Model/VILA-7b")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Efficient-Large-Model/VILA-7b", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Efficient-Large-Model/VILA-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Efficient-Large-Model/VILA-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Efficient-Large-Model/VILA-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Efficient-Large-Model/VILA-7b
- SGLang
How to use Efficient-Large-Model/VILA-7b 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 "Efficient-Large-Model/VILA-7b" \ --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": "Efficient-Large-Model/VILA-7b", "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 "Efficient-Large-Model/VILA-7b" \ --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": "Efficient-Large-Model/VILA-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Efficient-Large-Model/VILA-7b with Docker Model Runner:
docker model run hf.co/Efficient-Large-Model/VILA-7b
Image limit
#2
by swtb - opened
Is there a limit to how many images can be supplied as context?
Is there a limit to how many images can be supplied as context
First, please use VILA1.5 models. that will give you better performance
Second, for VILA1.5 models, in theory you can fit in 20 images for 8B (196 tokens/image and 4096 context length) but we only tested in-context learning w/ 2 images and video input w/ 8 frames.
klldmofashi changed discussion status to closed
klldmofashi changed discussion status to open
Thank you for the information π
swtb changed discussion status to closed