Instructions to use sujitvasanth/OpenCUA7BQfp164bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sujitvasanth/OpenCUA7BQfp164bit with Transformers:

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

pipe = pipeline("image-text-to-text", model="sujitvasanth/OpenCUA7BQfp164bit", 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)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sujitvasanth/OpenCUA7BQfp164bit", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sujitvasanth/OpenCUA7BQfp164bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sujitvasanth/OpenCUA7BQfp164bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sujitvasanth/OpenCUA7BQfp164bit",
		"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/sujitvasanth/OpenCUA7BQfp164bit

SGLang

How to use sujitvasanth/OpenCUA7BQfp164bit 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 "sujitvasanth/OpenCUA7BQfp164bit" \
    --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": "sujitvasanth/OpenCUA7BQfp164bit",
		"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 "sujitvasanth/OpenCUA7BQfp164bit" \
        --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": "sujitvasanth/OpenCUA7BQfp164bit",
		"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 sujitvasanth/OpenCUA7BQfp164bit with Docker Model Runner:
```
docker model run hf.co/sujitvasanth/OpenCUA7BQfp164bit
```

OpenCUA7BQfp164bit / configuration_opencua.py

sujitvasanth

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3e9b5d8 verified 8 months ago

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	from transformers.configuration_utils import PretrainedConfig
	from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLVisionConfig
	from transformers.models.qwen2.configuration_qwen2 import Qwen2Config


	class OpenCUAConfig(PretrainedConfig):
	"""OpenCUA-2.5-7B model configuration.

	Args:
	vision_config: Configuration for the vision model.Qwen2_5_VLVisionConfig
	text_config: Configuration for the text model. Qwen2Config
	pad_token_id: The token ID to use for padding.
	"""

	model_type = "opencua"

	def __init__(
	self,
	vision_config: dict \| Qwen2_5_VLVisionConfig \| None = None,
	text_config: dict \| Qwen2Config \| None = None,
	ignore_index: int = -100,
	media_placeholder_token_id: int = 151664,
	pad_token_id: int = 0,
	**kwargs
	):
	if isinstance(vision_config, dict):
	vision_config = Qwen2_5_VLVisionConfig(**vision_config)
	self.vision_config = vision_config

	if isinstance(text_config, dict):
	text_config = Qwen2Config(**text_config)
	self.text_config = text_config

	self.ignore_index = ignore_index
	self.media_placeholder_token_id = media_placeholder_token_id

	super().__init__(pad_token_id=pad_token_id, **kwargs)