Instructions to use Amazon-FAR/stopwatch-perception-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Amazon-FAR/stopwatch-perception-4b with Transformers:

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

pipe = pipeline("image-text-to-text", model="Amazon-FAR/stopwatch-perception-4b")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Amazon-FAR/stopwatch-perception-4b")
model = AutoModelForMultimodalLM.from_pretrained("Amazon-FAR/stopwatch-perception-4b", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Amazon-FAR/stopwatch-perception-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Amazon-FAR/stopwatch-perception-4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Amazon-FAR/stopwatch-perception-4b",
		"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/Amazon-FAR/stopwatch-perception-4b

SGLang

How to use Amazon-FAR/stopwatch-perception-4b 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 "Amazon-FAR/stopwatch-perception-4b" \
    --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": "Amazon-FAR/stopwatch-perception-4b",
		"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 "Amazon-FAR/stopwatch-perception-4b" \
        --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": "Amazon-FAR/stopwatch-perception-4b",
		"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 Amazon-FAR/stopwatch-perception-4b with Docker Model Runner:
```
docker model run hf.co/Amazon-FAR/stopwatch-perception-4b
```

stopwatch-perception-4b / README.md

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metadata

license: apache-2.0
library_name: transformers
base_model: Qwen/Qwen3-VL-4B-Instruct
tags:
  - object-navigation
  - safetensors
  - arxiv:2610.23841

StopWatch perception

Draft model card.

The 4B perception model predicts explicit goal grounding from RGB observations, including a semantic inventory, goal visibility and goal distance. It forms one navigation pipeline with the 2B policy, which reads the grounding and observations to choose discrete navigation actions.

Base model

Initialized from Qwen/Qwen3-VL-4B-Instruct at revision ebb281ec70b05090aa6165b016eac8ec08e71b17, as recorded in the StopWatch training recipe. Weights use bf16 safetensors in the stock Qwen3VLForConditionalGeneration layout.

Loading

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

repo_id = "Amazon-FAR/stopwatch-perception-4b"
model = AutoModelForImageTextToText.from_pretrained(
    repo_id, dtype=torch.bfloat16
)
processor = AutoProcessor.from_pretrained(repo_id)

The full pipeline code is available at amazon-far/stopwatch. Use the StopWatch code for prompt construction and Habitat-Lab evaluation. Perception performs full prefill of the latest 128 frames every step.

Pipeline results

These are paired perception-and-policy results from the paper evaluation archive on the released clean open-vocabulary benchmarks. Our criterion requires STOP with geodesic distance below 0.25 m to any instance viewpoint of an accepted goal class under our vocabulary, measured on the Stretch protocol navmesh. Clean subsets exclude annotation failures and empty or unreachable destination sets.

Benchmark	Successes / episodes	Success rate
OVON val_unseen clean	1606 / 2309	69.6%
MP3D-OV	595 / 1193	49.9%
HSSD-OV	988 / 1751	56.4%

Training data

OVON, MP3D and HSSD frontier-exploration trajectories. See Amazon-FAR/stopwatch-data.

License

Apache 2.0.

Citation

Paper: StopWatch: Goal Grounding for Object-goal Navigation.

@article{liu2026stopwatch,
  title={StopWatch: Goal Grounding for Object-goal Navigation},
  author={Liu, Xinhao and Tian, Yunsheng and Qiu, Yuheng and Abbeel, Pieter and Chen, Xi and Shi, Guanya and Feng, Chen},
  journal={arXiv preprint arXiv:2610.23841},
  year={2026}
}