Instructions to use stanfordasl/nuscenes-waypoints-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stanfordasl/nuscenes-waypoints-model with Transformers:

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

pipe = pipeline("image-text-to-text", model="stanfordasl/nuscenes-waypoints-model")
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("stanfordasl/nuscenes-waypoints-model")
model = AutoModelForMultimodalLM.from_pretrained("stanfordasl/nuscenes-waypoints-model")
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 stanfordasl/nuscenes-waypoints-model with vLLM:

Install from pip and serve model

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

SGLang

How to use stanfordasl/nuscenes-waypoints-model 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 "stanfordasl/nuscenes-waypoints-model" \
    --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": "stanfordasl/nuscenes-waypoints-model",
		"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 "stanfordasl/nuscenes-waypoints-model" \
        --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": "stanfordasl/nuscenes-waypoints-model",
		"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 stanfordasl/nuscenes-waypoints-model with Docker Model Runner:
```
docker model run hf.co/stanfordasl/nuscenes-waypoints-model
```

nuScenes Waypoints Model (Trajectory-Only)

Part of the RnB-EnCoRe-SelfDriving collection from the Stanford Autonomous Systems Lab.

📄 Paper: https://arxiv.org/abs/2602.08167
💻 Code / usage: https://github.com/rnb-encore/RnB-EnCoRe-SelfDriving

This is a Qwen3-VL-4B vision-language model fine-tuned on nuScenes driving data to directly predict a future waypoint trajectory from multi-camera observations, without producing intermediate natural-language reasoning.

For the reasoning + waypoints variant, see stanfordasl/nuscenes-rnbencore-reasoning-waypoints.

Model details

Base model: Qwen/Qwen3-VL-4B-Instruct (Qwen3VLForConditionalGeneration)
Architecture: hidden size 2560, 36 layers
Modality: image/video + text → text
Task: future trajectory (waypoint) prediction on nuScenes
Output: predicted waypoints (no chain-of-thought)

Training

Fine-tuned on a nuScenes VQA trajectory-only dataset
Epochs: 30 (10,980 optimizer steps)
Max sequence length: 2048

Usage

For dataset preparation, prompting, inference, and evaluation, follow the instructions in the project repository: https://github.com/rnb-encore/RnB-EnCoRe-SelfDriving

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "stanfordasl/nuscenes-waypoints-model"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

# Build a chat message with the driving camera image(s) + prompt,
# then processor.apply_chat_template(...) and model.generate(...).
# See the GitHub repo for the exact prompt format and post-processing.

Intended use & limitations

This model is a research artifact for autonomous-driving planning experiments. It was trained on nuScenes and is not intended for deployment in real vehicles or safety-critical settings. Outputs may be inaccurate or unsafe; always validate in simulation before any downstream use.