Image-Text-to-Text
Transformers
Safetensors
English
Arabic
qwen3_vl
multimodal
vision-language
secure
enterprise
on-premise
conversational
Instructions to use XCurOS/XCurOS1.2-8B-VLBF16-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XCurOS/XCurOS1.2-8B-VLBF16-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="XCurOS/XCurOS1.2-8B-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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("XCurOS/XCurOS1.2-8B-VLBF16-Instruct") model = AutoModelForMultimodalLM.from_pretrained("XCurOS/XCurOS1.2-8B-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 Settings
- vLLM
How to use XCurOS/XCurOS1.2-8B-VLBF16-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "XCurOS/XCurOS1.2-8B-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/XCurOS1.2-8B-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/XCurOS1.2-8B-VLBF16-Instruct
- SGLang
How to use XCurOS/XCurOS1.2-8B-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/XCurOS1.2-8B-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/XCurOS1.2-8B-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/XCurOS1.2-8B-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/XCurOS1.2-8B-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/XCurOS1.2-8B-VLBF16-Instruct with Docker Model Runner:
docker model run hf.co/XCurOS/XCurOS1.2-8B-VLBF16-Instruct
| license: other | |
| library_name: transformers | |
| tags: | |
| - multimodal | |
| - vision-language | |
| - secure | |
| - enterprise | |
| - on-premise | |
| language: | |
| - en | |
| - ar | |
| pipeline_tag: image-text-to-text | |
| # 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 | |
| - π **Security-First Design** | |
| Engineered to operate safely in isolated and sensitive environments, ensuring full data privacy and integrity. | |
| - π§ **Advanced Multimodal Intelligence** | |
| Combines text and visual perception for high-quality reasoning and understanding tasks. | |
| - π₯ **Agent & System Integration Ready** | |
| Compatible with operating system interfaces, automation pipelines, and agent workflows. | |
| - π **Document & OCR Capabilities** | |
| Extracts and interprets text from images, scanned documents, and complex layouts efficiently. | |
| - π― **Instruction-Tuned Performance** | |
| Fine-tuned to execute instructions accurately and reliably. | |
| - β‘ **High Efficiency & Scalable Deployment** | |
| Optimized for local machines and cloud infrastructure with efficient memory usage and inference speed. | |
| - π **Long-Context & Large-Scale Reasoning** | |
| Capable of handling large documents, books, and extended multi-modal datasets with coherent understanding. | |
| - π§© **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) |