Instructions to use HumanBehaviorAtlas/OmniSapiens2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HumanBehaviorAtlas/OmniSapiens2.0 with Transformers:

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

pipe = pipeline("image-text-to-text", model="HumanBehaviorAtlas/OmniSapiens2.0")
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 AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("HumanBehaviorAtlas/OmniSapiens2.0")
model = AutoModelForImageTextToText.from_pretrained("HumanBehaviorAtlas/OmniSapiens2.0")
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 = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HumanBehaviorAtlas/OmniSapiens2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HumanBehaviorAtlas/OmniSapiens2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HumanBehaviorAtlas/OmniSapiens2.0",
		"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/HumanBehaviorAtlas/OmniSapiens2.0

SGLang

How to use HumanBehaviorAtlas/OmniSapiens2.0 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 "HumanBehaviorAtlas/OmniSapiens2.0" \
    --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": "HumanBehaviorAtlas/OmniSapiens2.0",
		"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 "HumanBehaviorAtlas/OmniSapiens2.0" \
        --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": "HumanBehaviorAtlas/OmniSapiens2.0",
		"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 HumanBehaviorAtlas/OmniSapiens2.0 with Docker Model Runner:
```
docker model run hf.co/HumanBehaviorAtlas/OmniSapiens2.0
```

keentomato commited on 9 days ago

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@@ -11,17 +11,17 @@ paper: https://arxiv.org/abs/2602.10635
 Omnisapiens 2.0 is a model developed with reference to the following paper:
-📄 **Paper:** [arXiv:2602.10635](https://arxiv.org/abs/2602.10635)
 ## Citation
 If you use this model, please cite the associated paper:
-\`\`\`bibtex
 @article{ong2026omnisapiens,
   title={Omnisapiens: A foundation model for social behavior processing via heterogeneity-aware relative policy optimization},
   author={Ong, Keane and Boughorbel, Sabri and Xiao, Luwei and Ekbote, Chanakya and Dai, Wei and Qu, Ao and Wu, Jingyao and Mao, Rui and Hoque, Ehsan and Cambria, Erik and others},
   journal={arXiv preprint arXiv:2602.10635},
   year={2026}
 }
-\`\`\`

 Omnisapiens 2.0 is a model developed with reference to the following paper:
+📄 **Omnisapiens: A foundation model for social behavior processing via heterogeneity-aware relative policy optimization:** [arXiv:2602.10635](https://arxiv.org/abs/2602.10635)
 ## Citation
 If you use this model, please cite the associated paper:
+```bibtex
 @article{ong2026omnisapiens,
   title={Omnisapiens: A foundation model for social behavior processing via heterogeneity-aware relative policy optimization},
   author={Ong, Keane and Boughorbel, Sabri and Xiao, Luwei and Ekbote, Chanakya and Dai, Wei and Qu, Ao and Wu, Jingyao and Mao, Rui and Hoque, Ehsan and Cambria, Erik and others},
   journal={arXiv preprint arXiv:2602.10635},
   year={2026}
 }
+```