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
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license: other
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- deconav
- embodied-navigation
- vision-language-navigation
- multi-robot
---
# DeCoNav Release Assets
This repository contains the external model and perception assets required by
the DeCoNav evaluator. It is designed to be downloaded into the `assets/`
directory of the DeCoNav source repository.
## Contents
```text
models/
deconav_nav_policy/ # Fine-tuned Qwen3-VL navigation policy
deconav_eva_clip.pt # EVA-CLIP visual encoder checkpoint
perception/
deconav_clip.pt # Fine-tuned object-recognition checkpoint
deconav_clip_thresholds.json
deconav_room_classifier/
config.json
best_model.pt
```
Training state, optimizer state, RNG state, scheduler files, and conversion
utilities are intentionally excluded. The source repository validates every
required file before evaluation starts.
## Usage
```bash
hf download Sunyao/deconav --repo-type model --revision REVISION \
--local-dir assets
```
Replace `REVISION` with the immutable revision recorded in the DeCoNav
`ASSET_MANIFEST.json`. The runtime uses local files only and does not enable
`trust_remote_code`.
## Intended Use
These assets are intended for research reproduction of the five DeCoNav
evaluation modes on the released task manifest. They are not a general-purpose
robot-control system and must not be used as a safety-critical controller.
## License
The navigation checkpoint is a derivative of Qwen model materials and is
distributed under the Qwen Research License included as `LICENSE`. That
license contains non-commercial, redistribution, attribution, and other use
conditions. EVA-CLIP and other perception components also retain their
upstream terms. The MIT license of the DeCoNav source repository does not
relicense these weights.
## Evaluation
The published 100-task Full reference result is BSR 40.0%, OSR 50.0%, ISR
58.5%, and SPL 0.369752. Detailed result provenance is published with the
matching DeCoNav source release.
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