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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---
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](https://huggingface.co/Amazon-FAR/stopwatch-policy-2b), 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

```python
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](https://github.com/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](https://huggingface.co/datasets/Amazon-FAR/stopwatch-data).
## License
Apache 2.0.
## Citation
Paper: [StopWatch: Goal Grounding for Object-goal Navigation](https://arxiv.org/abs/2610.23841).
```bibtex
@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}
}
```

	---
	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](https://huggingface.co/Amazon-FAR/stopwatch-policy-2b), 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

	```python
	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](https://github.com/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](https://huggingface.co/datasets/Amazon-FAR/stopwatch-data).

	## License

	Apache 2.0.

	## Citation

	Paper: [StopWatch: Goal Grounding for Object-goal Navigation](https://arxiv.org/abs/2610.23841).

	```bibtex
	@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}
	}
	```