Instructions to use Efficient-Large-Model/NVILA-15B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Efficient-Large-Model/NVILA-15B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Efficient-Large-Model/NVILA-15B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Efficient-Large-Model/NVILA-15B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Efficient-Large-Model/NVILA-15B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Efficient-Large-Model/NVILA-15B" # 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/NVILA-15B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Efficient-Large-Model/NVILA-15B
- SGLang
How to use Efficient-Large-Model/NVILA-15B 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/NVILA-15B" \ --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/NVILA-15B", "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/NVILA-15B" \ --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/NVILA-15B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Efficient-Large-Model/NVILA-15B with Docker Model Runner:
docker model run hf.co/Efficient-Large-Model/NVILA-15B
GGUF conversion fails
Hello!
I'm trying to use VILA / NVILA / LongVILA models in Ollama + OpenWebUI.
My problem is that when trying to use e.g ggml-org/gguf-my-repo or other GGUF converters, they always fail;
the error traces back to Llama.cpp backend and it says:
"Error converting to fp16: INFO:hf-to-gguf:Loading model: NVILA-15B
ERROR:hf-to-gguf:Model LlavaLlamaModel is not supported"
Llama.cpp doesn't seem to like 'LlavaLlamaModel' as a base-model.
(basically llama_cpp/convert_hf_to_gguf.py throws a NotImplementedError)
Can you offer any help with this?
I'd really appreciate any tips, or, best case scenario, .gguf files offered under your projects.