Instructions to use AppliedIntuitionResearch/nord with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AppliedIntuitionResearch/nord with Transformers:

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

pipe = pipeline("image-text-to-text", model="AppliedIntuitionResearch/nord")
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("AppliedIntuitionResearch/nord")
model = AutoModelForMultimodalLM.from_pretrained("AppliedIntuitionResearch/nord", 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 AppliedIntuitionResearch/nord with vLLM:

Install from pip and serve model

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

SGLang

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

shubh-gupta-ai commited on Aug 4

Commit

7e7a7da

verified ·

1 Parent(s): b959c51

Set license to CC BY-NC-SA 4.0

Browse files

Files changed (1) hide show

README.md +10 -7

README.md CHANGED Viewed

@@ -1,5 +1,5 @@
 ---
-license: apache-2.0
 base_model: Qwen/Qwen2.5-VL-3B-Instruct
 pipeline_tag: image-text-to-text
 library_name: transformers
@@ -13,13 +13,13 @@ tags:
 # NoRD: No Reasoning for Driving (SFT + Dr. GRPO)
-**CVPR 2026** | [arXiv](https://arxiv.org/abs/2602.21172) | [Project Page](https://nord-vla-ai.github.io/) | [GitHub](https://github.com/Applied-Intuition-Open-Source/nord)
 *Ishaan Rawal · Shubh Gupta · Yihan Hu · Wei Zhan*
-[Qwen2.5-VL-3B](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)
 supervised fine-tuned and then further trained with Dr. GRPO to directly predict driving
-trajectories as discrete tokens without chain-of-thought reasoning, using 3× fewer tokens than
 reasoning-based VLA baselines.
 | Model | Training | NAVSIM PDMS |
@@ -38,9 +38,10 @@ PDMS                                 0.8626
 ## Usage
-Install the [`nord`](https://github.com/Applied-Intuition-Open-Source/nord) client and serve with vLLM:
 ```bash
 vllm serve AppliedIntuitionResearch/nord --served-model-name qwen --dtype bfloat16 --port 8000
 ```
@@ -59,7 +60,7 @@ output = agent.predict(nord.NordInput(
 print(output.trajectory.shape)   # (40, 3) — x, y, heading at 10 Hz, 4 seconds
 ```
-Full inference and NAVSIM evaluation instructions: [github.com/nord-vla-ai/nord](https://github.com/Applied-Intuition-Open-Source/nord).
 This repo bundles the K-Disc trajectory tokenizer vocab (`vocab.pkl`, 2048 clusters) used to
 decode the model's output tokens into `(x, y, heading)` trajectories.
@@ -77,4 +78,6 @@ decode the model's output tokens into `(x, y, heading)` trajectories.
 ## License
-Apache 2.0.

 ---
+license: cc-by-nc-sa-4.0
 base_model: Qwen/Qwen2.5-VL-3B-Instruct
 pipeline_tag: image-text-to-text
 library_name: transformers
 # NoRD: No Reasoning for Driving (SFT + Dr. GRPO)
+**CVPR 2026** | [arXiv](https://arxiv.org/abs/2602.21172) | [Project Page](https://nord-vla-ai.github.io/) | [GitHub](https://github.com/nord-vla-ai/nord)
 *Ishaan Rawal · Shubh Gupta · Yihan Hu · Wei Zhan*
+This is the **paper's main result**: [Qwen2.5-VL-3B](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)
 supervised fine-tuned and then further trained with Dr. GRPO to directly predict driving
+trajectories as discrete tokens — no chain-of-thought reasoning, 3× fewer tokens than
 reasoning-based VLA baselines.
 | Model | Training | NAVSIM PDMS |
 ## Usage
+Install the [`nord`](https://github.com/nord-vla-ai/nord) client and serve with vLLM:
 ```bash
+pip install "nord[serve]"
 vllm serve AppliedIntuitionResearch/nord --served-model-name qwen --dtype bfloat16 --port 8000
 ```
 print(output.trajectory.shape)   # (40, 3) — x, y, heading at 10 Hz, 4 seconds
 ```
+Full inference and NAVSIM evaluation instructions: [github.com/nord-vla-ai/nord](https://github.com/nord-vla-ai/nord).
 This repo bundles the K-Disc trajectory tokenizer vocab (`vocab.pkl`, 2048 clusters) used to
 decode the model's output tokens into `(x, y, heading)` trajectories.
 ## License
+This checkpoint is released under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+(non-commercial). The `nord` inference client code is separately licensed under Apache 2.0 —
+see [github.com/nord-vla-ai/nord](https://github.com/nord-vla-ai/nord).