Image-Text-to-Text
Transformers
Safetensors
English
qwen2_5_vl
vision-language
vision-language-navigation
embodied-ai
robotics
qwen2.5-vl
reinforcement-learning
grpo
conversational
text-generation-inference
Instructions to use UnderTides/Embodied-Navigator-7B-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UnderTides/Embodied-Navigator-7B-GRPO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="UnderTides/Embodied-Navigator-7B-GRPO") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("UnderTides/Embodied-Navigator-7B-GRPO") model = AutoModelForMultimodalLM.from_pretrained("UnderTides/Embodied-Navigator-7B-GRPO", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use UnderTides/Embodied-Navigator-7B-GRPO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UnderTides/Embodied-Navigator-7B-GRPO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UnderTides/Embodied-Navigator-7B-GRPO", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/UnderTides/Embodied-Navigator-7B-GRPO
- SGLang
How to use UnderTides/Embodied-Navigator-7B-GRPO 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 "UnderTides/Embodied-Navigator-7B-GRPO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UnderTides/Embodied-Navigator-7B-GRPO", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "UnderTides/Embodied-Navigator-7B-GRPO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UnderTides/Embodied-Navigator-7B-GRPO", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use UnderTides/Embodied-Navigator-7B-GRPO with Docker Model Runner:
docker model run hf.co/UnderTides/Embodied-Navigator-7B-GRPO
Update pipeline tag and paper link
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base_model: Qwen/Qwen2.5-VL-7B-Instruct
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library_name: transformers
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pipeline_tag: image-text-to-text
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language:
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tags:
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[Project Page](https://zju-omniai.github.io/Embodied-Navigator/) |
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[Code](https://github.com/ZJU-OmniAI/Embodied-Navigator) |
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[Paper](https://
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Embodied-Navigator-7B-GRPO is the released navigation policy checkpoint for
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Embodied-Navigator. It adapts Qwen2.5-VL-7B to continuous vision-language
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Hongyan Feng, Sunlai Chen, Xuanyu Liu, Miao Pan, Yangfan Xie, Yuxiang Cui,
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Zhongxiang Zhou, Rong Xiong, Wenqi Zhang, Jianwei Yin, Yueting Zhuang, and
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Xuhong Zhang.
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base_model: Qwen/Qwen2.5-VL-7B-Instruct
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language:
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library_name: transformers
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pipeline_tag: robotics
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tags:
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- vision-language
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- vision-language-navigation
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[Project Page](https://zju-omniai.github.io/Embodied-Navigator/) |
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[Code](https://github.com/ZJU-OmniAI/Embodied-Navigator) |
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[Paper](https://huggingface.co/papers/2608.17512)
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Embodied-Navigator-7B-GRPO is the released navigation policy checkpoint for
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Embodied-Navigator. It adapts Qwen2.5-VL-7B to continuous vision-language
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Hongyan Feng, Sunlai Chen, Xuanyu Liu, Miao Pan, Yangfan Xie, Yuxiang Cui,
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Zhongxiang Zhou, Rong Xiong, Wenqi Zhang, Jianwei Yin, Yueting Zhuang, and
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Xuhong Zhang.
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