Instructions to use m-ric/Aria_hf_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use m-ric/Aria_hf_2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="m-ric/Aria_hf_2", trust_remote_code=True)
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("m-ric/Aria_hf_2", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("m-ric/Aria_hf_2", trust_remote_code=True, 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 m-ric/Aria_hf_2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "m-ric/Aria_hf_2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "m-ric/Aria_hf_2",
		"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/m-ric/Aria_hf_2

SGLang

How to use m-ric/Aria_hf_2 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 "m-ric/Aria_hf_2" \
    --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": "m-ric/Aria_hf_2",
		"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 "m-ric/Aria_hf_2" \
        --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": "m-ric/Aria_hf_2",
		"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 m-ric/Aria_hf_2 with Docker Model Runner:
```
docker model run hf.co/m-ric/Aria_hf_2
```

Aria_hf_2

File size: 1,212 Bytes

{
  "_transform": null,
  "auto_map": {
    "AutoImageProcessor": "rhymes-ai/Aria--vision_processor.AriaVisionProcessor",
    "AutoProcessor": "rhymes-ai/Aria--processing_aria.AriaProcessor"
  },
  "do_convert_rgb": true,
  "do_normalize": true,
  "image_mean": [
    0.5,
    0.5,
    0.5
  ],
  "image_processor_type": "AriaImageProcessor",
  "image_std": [
    0.5,
    0.5,
    0.5
  ],
  "max_image_size": 980,
  "min_image_size": 336,
  "processor_class": "AriaProcessor",
  "resample": 3,
  "split_image": false,
  "split_resolutions": [
    [
      490,
      980
    ],
    [
      490,
      1470
    ],
    [
      490,
      1960
    ],
    [
      490,
      2450
    ],
    [
      490,
      2940
    ],
    [
      490,
      3430
    ],
    [
      490,
      3920
    ],
    [
      980,
      1960
    ],
    [
      980,
      1470
    ],
    [
      980,
      980
    ],
    [
      980,
      490
    ],
    [
      1470,
      490
    ],
    [
      1470,
      980
    ],
    [
      1960,
      490
    ],
    [
      1960,
      980
    ],
    [
      2450,
      490
    ],
    [
      2940,
      490
    ],
    [
      3430,
      490
    ],
    [
      3920,
      490
    ]
  ]
}