Image-Text-to-Text
Transformers
Safetensors
deconav
embodied-navigation
vision-language-navigation
multi-robot
Instructions to use Sunyao/deconav with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sunyao/deconav with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Sunyao/deconav")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sunyao/deconav", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Sunyao/deconav with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Sunyao/deconav" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sunyao/deconav", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Sunyao/deconav
- SGLang
How to use Sunyao/deconav 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 "Sunyao/deconav" \ --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": "Sunyao/deconav", "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 "Sunyao/deconav" \ --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": "Sunyao/deconav", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Sunyao/deconav with Docker Model Runner:
docker model run hf.co/Sunyao/deconav
| { | |
| "room_categories": [ | |
| "Bathroom", | |
| "Bedroom", | |
| "Closet", | |
| "Dining Room", | |
| "Hallway", | |
| "Kitchen", | |
| "Laundry Room", | |
| "Living Room", | |
| "Office", | |
| "Utility Room", | |
| "Garage", | |
| "Pantry", | |
| "Gym", | |
| "Porch", | |
| "TV Room", | |
| "Storage Room", | |
| "Dressing Room", | |
| "Space Room" | |
| ], | |
| "num_classes": 18, | |
| "input_dim": 768, | |
| "hidden_dims": [ | |
| 2048, | |
| 1024, | |
| 512, | |
| 256 | |
| ], | |
| "dropout": 0.0, | |
| "best_val_acc": 1.0, | |
| "fit_mode": "stop_overfit", | |
| "best_seed": 42, | |
| "best_epoch": 26 | |
| } |