Instructions to use TidalYang/SNav-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TidalYang/SNav-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="TidalYang/SNav-7B")
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, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("TidalYang/SNav-7B")
model = AutoModelForCausalLM.from_pretrained("TidalYang/SNav-7B", 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 TidalYang/SNav-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TidalYang/SNav-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TidalYang/SNav-7B",
		"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/TidalYang/SNav-7B

SGLang

How to use TidalYang/SNav-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 "TidalYang/SNav-7B" \
    --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": "TidalYang/SNav-7B",
		"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 "TidalYang/SNav-7B" \
        --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": "TidalYang/SNav-7B",
		"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 TidalYang/SNav-7B with Docker Model Runner:
```
docker model run hf.co/TidalYang/SNav-7B
```

SNav-7B

SNav-7B is the navigation baseline from NavSpace: How Navigation Agents Follow Spatial Intelligence Instructions (ICRA 2026).

Architecture: LLaVA-Video / Qwen2-7B + SigLIP (LlavaQwenForCausalLM)
Vision tower: google/siglip-so400m-patch14-384
Code & benchmark: TidalHarley/NavSpace
Project page: https://navspace.github.io/

Quick start

# download
huggingface-cli download TidalYang/SNav-7B --local-dir ./SNav-7B

# evaluate on NavSpace (see NavSpace repo docs)
python evaluation/eval_snav.py \
  --model-path ./SNav-7B \
  --vision-tower-path /path/to/siglip-so400m-patch14-384 \
  --hm3d-base-path /path/to/hm3d_v0.2 \
  --task environment_state

Loading uses the LLaVA / StreamVLN-style stack from the NavSpace repository (not plain transformers.AutoModel alone).

Citation

@misc{yang2026navspacenavigationagentsfollow,
  title={NavSpace: How Navigation Agents Follow Spatial Intelligence Instructions},
  author={Haolin Yang and Yuxing Long and Zhuoyuan Yu and Zihan Yang and Minghan Wang and Jiapeng Xu and Yihan Wang and Ziyan Yu and Wenzhe Cai and Lei Kang and Hao Dong},
  year={2026},
  eprint={2510.08173},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2510.08173}
}