Instructions to use mjf-su/PhysicalAI-base-VLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mjf-su/PhysicalAI-base-VLA with Transformers:

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

pipe = pipeline("image-text-to-text", model="mjf-su/PhysicalAI-base-VLA")
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("mjf-su/PhysicalAI-base-VLA")
model = AutoModelForMultimodalLM.from_pretrained("mjf-su/PhysicalAI-base-VLA")
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 mjf-su/PhysicalAI-base-VLA with vLLM:

Install from pip and serve model

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

SGLang

How to use mjf-su/PhysicalAI-base-VLA 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 "mjf-su/PhysicalAI-base-VLA" \
    --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": "mjf-su/PhysicalAI-base-VLA",
		"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 "mjf-su/PhysicalAI-base-VLA" \
        --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": "mjf-su/PhysicalAI-base-VLA",
		"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 mjf-su/PhysicalAI-base-VLA with Docker Model Runner:
```
docker model run hf.co/mjf-su/PhysicalAI-base-VLA
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

PhysicalAI-reason-VLA

A vision-language driving policy fine-tuned from mjf-su/PhysicalAI-base-VLA (itself based on Qwen/Qwen3-VL-4B-Thinking) using supervised fine-tuning with TRL.

This model extends the base waypoint-prediction VLA with structured chain-of-thought reasoning and discrete driving decisions, trained on 10k Gemini-annotated driving scenes for 2 epochs.

Input / Output

Inputs

A forward-facing camera image
Past ego-vehicle waypoints in the vehicle's relative frame

Output

<think>
{
  "scene": "2–3 sentence static scene description",
  "move_justification": "2–3 sentence causal explanation linking scene to decisions",
}
</think>
<action>
<longitudinal_token><lateral_token>
</action>
<wp>[x.xx,y.yy,t.tttt]</wp>
<wp>[x.xx,y.yy,t.tttt]</wp>
...

The model produces three outputs in sequence: a reasoning trace (<think>), discrete longitudinal and lateral driving decisions (<action>), and future trajectory waypoints (<wp>).

Decision Tokens

Each <action> block contains exactly one longitudinal and one lateral token.

Longitudinal — <stop> · <yield> · <follow> · <gap_search> · <pass> · <adapt> · <cruise>

Lateral — <turn_left> · <turn_right> · <lc_left> · <lc_right> · <merge> · <nudge_out_left> · <nudge_out_right> · <nudge_in_left> · <nudge_in_right> · <pull_over> · <abort> · <lane_keep>

These are registered as genuine single tokens in the vocabulary (not subword decompositions), enabling efficient probability measurement over the full decision space with a single forward pass.

Training


Base model	mjf-su/PhysicalAI-base-VLA
Dataset	mjf-su/PhysicalAI-reason-US
Annotation	Gemini batch API (chain-of-thought labels on real US driving data)
Samples	10,000
Epochs	2
Method	Completion-only SFT via TRL

Quick Start

from transformers import AutoProcessor, AutoModelForImageTextToText
from PIL import Image

model_id = "mjf-su/PhysicalAI-reason-VLA"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, device_map="auto")

image = Image.open("forward_camera.jpg")

past_waypoints = "<wp>[0.00,0.00,0.0000]</wp>\n<wp>[0.51,0.00,0.0001]</wp>\n..."

messages = [
    {
        "role": "system",
        "content": [{"type": "text", "text": "You are a helpful AI assistant ..."}]
    },
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": f"[PAST-VEHICLE-MOTION]:\n{past_waypoints}"}
        ]
    }
]

prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[prompt], images=[image], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(processor.batch_decode(outputs, skip_special_tokens=True)[0])

Citation

@misc{vonwerra2022trl,
  title        = {{TRL: Transformer Reinforcement Learning}},
  author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching
                  and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul
                  and Quentin Gallou{\'e}dec},
  year         = 2022,
  journal      = {GitHub repository},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/huggingface/trl}}
}

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for mjf-su/PhysicalAI-base-VLA

Base model

Qwen/Qwen3-VL-4B-Thinking

Finetuned

(23)

this model

Quantizations

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