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

Libraries

How to use AppliedIntuitionResearch/nord-base with Transformers:

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

pipe = pipeline("image-text-to-text", model="AppliedIntuitionResearch/nord-base")
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("AppliedIntuitionResearch/nord-base")
model = AutoModelForMultimodalLM.from_pretrained("AppliedIntuitionResearch/nord-base", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AppliedIntuitionResearch/nord-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AppliedIntuitionResearch/nord-base"
# 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-base",
		"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-base

SGLang

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

shubh-gupta-ai commited on 6 days ago

Commit

2395796

verified ·

1 Parent(s): e5dec44

Upload NoRD checkpoint

Browse files

Files changed (13) hide show

README.md +72 -0
added_tokens.json +2072 -0
config.json +65 -0
generation_config.json +13 -0
merges.txt +0 -0
model-00001-of-00002.safetensors +3 -0
model-00002-of-00002.safetensors +3 -0
model.safetensors.index.json +832 -0
preprocessor_config.json +29 -0
special_tokens_map.json +31 -0
tokenizer_config.json +0 -0
vocab.json +0 -0
vocab.pkl +3 -0

README.md ADDED Viewed

	@@ -0,0 +1,72 @@

+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-VL-3B-Instruct
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+  - autonomous-driving
+  - vision-language-action
+  - trajectory-prediction
+  - navsim
+  - vllm
+---
+# NoRD-Base: No Reasoning for Driving (SFT only)
+**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 **SFT-only ablation baseline** from the paper: [Qwen2.5-VL-3B](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)
+supervised fine-tuned to directly predict driving trajectories as discrete tokens — no
+chain-of-thought reasoning, 3× fewer tokens than reasoning-based VLA baselines. This checkpoint
+is also the initialization used for the Dr. GRPO stage that produces the paper's main result.
+| Model | Training | NAVSIM PDMS |
+|---|---|---|
+| [`nord`](https://huggingface.co/AppliedIntuitionResearch/nord) | SFT + Dr. GRPO | 0.8626 |
+| **`nord-base`** (this repo) | SFT only | **0.7273** |
+## 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-base --served-model-name qwen --dtype bfloat16 --port 8000
+```
+```python
+from PIL import Image
+import nord
+agent = nord.NordAgent.from_pretrained("nord-base")
+output = agent.predict(nord.NordInput(
+    cameras=[Image.open("fl.jpg"), Image.open("front.jpg"), Image.open("fr.jpg")],
+    ego_velocity_ms=(8.3, 0.0),
+    driving_command="straight",
+))
+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.
+## Citation
+```bibtex
+@inproceedings{rawal2026nord,
+  title={NoRD: A Data-Efficient Vision-Language-Action Model that Drives without Reasoning},
+  author={Rawal, Ishaan and Gupta, Shubh and Hu, Yihan and Zhan, Wei},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2026}
+}
+```
+## License
+Apache 2.0.

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tokenizer_config.json ADDED Viewed

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vocab.json ADDED Viewed

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