Instructions to use velora-intelligence/velora-ai-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use velora-intelligence/velora-ai-v1 with Transformers:

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

pipe = pipeline("text-generation", model="velora-intelligence/velora-ai-v1")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("velora-intelligence/velora-ai-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use velora-intelligence/velora-ai-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "velora-intelligence/velora-ai-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "velora-intelligence/velora-ai-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/velora-intelligence/velora-ai-v1

SGLang

How to use velora-intelligence/velora-ai-v1 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 "velora-intelligence/velora-ai-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "velora-intelligence/velora-ai-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "velora-intelligence/velora-ai-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "velora-intelligence/velora-ai-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use velora-intelligence/velora-ai-v1 with Docker Model Runner:
```
docker model run hf.co/velora-intelligence/velora-ai-v1
```

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VELORA AI

Overview

VELORA AI is a next-generation multilingual Artificial Intelligence platform designed to assist users through natural conversations, advanced reasoning, intelligent content generation, coding assistance, education, productivity, document understanding, image analysis, and voice interaction.

The long-term objective of VELORA AI is to evolve into a complete AI ecosystem that integrates modern language models, computer vision, speech technologies, document intelligence, and future multimodal capabilities into one unified platform.

The project is designed with scalability, modularity, security, and continuous improvement as its core principles.

Vision

Our vision is to build an independent AI platform that is:

Intelligent
Helpful
Explainable
Secure
Fast
Multilingual
Modular
Scalable
Privacy-focused
Continuously improving

VELORA AI aims to provide professional AI assistance for students, developers, engineers, businesses, researchers, educators, creators, and everyday users.

Core Capabilities

Natural Language AI

Natural conversations
Context awareness
Conversation history
Long-term memory architecture
Intelligent question answering
Advanced reasoning
Logical thinking
Knowledge retrieval
Text generation
Content rewriting
Summarization
Brainstorming
Creative writing

Coding Assistant

Python
Java
JavaScript
TypeScript
C
C++
C#
PHP
Go
Rust
Kotlin
Swift
SQL
HTML
CSS

Capabilities include:

Code generation
Code explanation
Code review
Debugging
Optimization
Refactoring
Documentation
API development

Mathematics

Basic Mathematics
Algebra
Geometry
Trigonometry
Statistics
Probability
Calculus
Linear Algebra

Science

Physics
Chemistry
Biology
Astronomy
Earth Science

Engineering

Civil Engineering
Mechanical Engineering
Electrical Engineering
Software Engineering
Architecture
Construction

Education

Homework assistance
Exam preparation
Lesson explanations
Research support
Learning plans
Study assistance

Writing

Professional writing
Academic writing
Emails
Reports
Articles
Essays
Documentation

Translation

Supported Languages

English
Arabic
French

Future versions will expand to many additional languages.

Image Intelligence

Planned support includes:

Image understanding
OCR
Object recognition
Scene understanding
Image captioning
Diagram interpretation

Document Intelligence

PDF analysis
Word documents
Text extraction
OCR
Document summarization
Knowledge extraction

Voice Intelligence

Speech Recognition
Speech-to-Text
Text-to-Speech
Voice Interaction
Multilingual Voice Support

Platform Architecture

Frontend

Web Application
Android Application
iOS Application
Desktop Application

Backend

Python
FastAPI
REST API
WebSocket
Authentication
Database Integration

AI Stack

Hugging Face Transformers
PyTorch
Tokenizers
Accelerate
Safetensors

Future planned integrations include additional open-source AI models and multimodal systems.

Future Roadmap

Phase 1

Platform foundation
Chat interface
Backend APIs

Phase 2

Voice interaction
Image understanding
OCR
Document analysis

Phase 3

AI agents
Tool calling
Knowledge retrieval
Multimodal capabilities

Phase 4

Mobile applications
Enterprise APIs
Cloud deployment
Performance optimization

Supported Platforms

Web
Android
iOS
Desktop

Goals

VELORA AI aims to deliver:

Helpful responses
Accurate information
Explainable outputs
Responsible AI behavior
Privacy-focused interactions
High-quality multilingual assistance
Continuous platform improvement

Current Status

Project Name

VELORA AI

Version

v1.0

Development Status

Early Development

Current Stage

Platform Architecture and Initial Development

License

Apache License 2.0

Acknowledgments

VELORA AI is built using modern open-source technologies including Python, FastAPI, Hugging Face Transformers, PyTorch, and other AI development tools. The project follows a modular architecture to enable continuous improvements and future expansion.

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