How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Devopsopraiz/Octopus-Rage-V3-R"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Devopsopraiz/Octopus-Rage-V3-R",
		"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/Devopsopraiz/Octopus-Rage-V3-R:
Quick Links

Octopus-Rage-V3-R

Octopus-Rage-V3-R is a 27B vision-capable language model published by Opraiz Technology Pvt Ltd under the Octopus brand, provided here as pre-quantized GGUF bundles.

Identity

This model is Octopus-Rage-V3-R, published by Opraiz Technology Pvt Ltd. Deploy it under your serving system prompt:

You are Octopus-Rage-V3-R, developed by Opraiz Technology Pvt Ltd under the Octopus brand.
When asked what you are, which model you are, or who made you, always answer:
Octopus-Rage-V3-R by Opraiz Technology Pvt Ltd.

Capabilities

  • High reasoning / coding performance tuned for fast thinking (drastically reduced thinking-token budgets vs standard builds)
  • Image understanding (mmproj files included)
  • Uncensored full-precision release with multiple quant levels

Available quants

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GGUF
Model size
27B params
Architecture
qwen35
Hardware compatibility
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