Instructions to use XCurOS/XCurOS-1.2-8B-VLBF16-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XCurOS/XCurOS-1.2-8B-VLBF16-Instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="XCurOS/XCurOS-1.2-8B-VLBF16-Instruct")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("XCurOS/XCurOS-1.2-8B-VLBF16-Instruct")
model = AutoModelForImageTextToText.from_pretrained("XCurOS/XCurOS-1.2-8B-VLBF16-Instruct")
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

vLLM

How to use XCurOS/XCurOS-1.2-8B-VLBF16-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XCurOS/XCurOS-1.2-8B-VLBF16-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XCurOS/XCurOS-1.2-8B-VLBF16-Instruct",
		"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/XCurOS/XCurOS-1.2-8B-VLBF16-Instruct

SGLang

How to use XCurOS/XCurOS-1.2-8B-VLBF16-Instruct 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 "XCurOS/XCurOS-1.2-8B-VLBF16-Instruct" \
    --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": "XCurOS/XCurOS-1.2-8B-VLBF16-Instruct",
		"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 "XCurOS/XCurOS-1.2-8B-VLBF16-Instruct" \
        --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": "XCurOS/XCurOS-1.2-8B-VLBF16-Instruct",
		"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 XCurOS/XCurOS-1.2-8B-VLBF16-Instruct with Docker Model Runner:
```
docker model run hf.co/XCurOS/XCurOS-1.2-8B-VLBF16-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

XCurOS-1.2-8B-VLBF16-Instruct

XCurOS Secure Vision-Language Model

XCurOS-1.2-8B-VLBF16-Instruct is a state-of-the-art proprietary multimodal model developed exclusively by XCurOS.
It provides advanced understanding and reasoning across text and visual inputs, designed for secure, enterprise-grade, and on-premise deployments.

Key Features

🔐 Security-First Design
Engineered to operate safely in isolated and sensitive environments, ensuring full data privacy and integrity.
🧠 Advanced Multimodal Intelligence
Combines text and visual perception for high-quality reasoning and understanding tasks.
🖥 Agent & System Integration Ready
Compatible with operating system interfaces, automation pipelines, and agent workflows.
📄 Document & OCR Capabilities
Extracts and interprets text from images, scanned documents, and complex layouts efficiently.
🎯 Instruction-Tuned Performance
Fine-tuned to execute instructions accurately and reliably.
⚡ High Efficiency & Scalable Deployment
Optimized for local machines and cloud infrastructure with efficient memory usage and inference speed.
🌐 Long-Context & Large-Scale Reasoning
Capable of handling large documents, books, and extended multi-modal datasets with coherent understanding.
🧩 Extensible & Modular Architecture
Easily integrated into custom applications, agent frameworks, and secure enterprise systems.

Architecture Overview

XCurOS-1.2-8B-VLBF16-Instruct architecture provides:

Text-vision fusion: Seamless integration of visual and textual information for robust reasoning.
Long-context processing: Maintains coherence across extended inputs and multi-modal datasets.
Stable instruction alignment: Ensures precise adherence to commands and tasks.
Flexible deployment: Adaptable from edge devices to cloud servers.

Use Cases

Secure AI assistants for sensitive enterprise data.
Enterprise system automation and orchestration.
Document understanding and knowledge extraction.
Visual analysis across diagrams, images, and scanned materials.
On-premise deployment where data privacy and intellectual property are critical.

Model Card & Metadata

Model Name: XCurOS-1.2-8B-VLBF16-Instruct
Organization: XCurOS
Version: 1.2
License: All rights reserved, proprietary software
Multimodal: True
Secure OS Ready: True
Intended Audience: Enterprises, research labs, and private organizations requiring secure AI solutions

Deployment Recommendations

Deploy locally within secure enterprise infrastructure for maximum privacy.
Integrate into automation pipelines or agent-based systems.
Ensure sufficient GPU/CPU resources for optimal performance, especially for long-context processing.

License & Usage

This is proprietary software. All rights are reserved by XCurOS.
No part of this model may be copied, redistributed, or used without explicit authorization from XCurOS.

Contribution & Support

XCurOS maintains this model internally.
For collaboration, licensing, or support, contact the XCurOS development team directly.
All contributions, enhancements, or integrations require explicit permission from XCurOS.

Author: 35H - (f13b696767b224479d7a06bffae9a0b62e38e2e2)

Downloads last month: 90,658

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for XCurOS/XCurOS-1.2-8B-VLBF16-Instruct

Quantizations

3 models