Nexora
Nexora is a lightweight Python toolkit for working with AI models and machine learning capabilities.
The project is designed as a modular AI ecosystem, providing a unified Python interface for different model families and capabilities.
Installation
Install Nexora from PyPI:
pip install nexora-ai
Quick Start
Optical Character Recognition
The OCR module provides a simple interface for extracting text from images.
from nexora.ocr import NexoraOCR
ocr = NexoraOCR()
result = ocr.read("image.png")
print(result)
Nexora automatically loads the selected OCR model when inference is requested.
Selecting an OCR Model
Nexora supports multiple OCR models through the model parameter.
Default Model
If no model is specified, Nexora uses the 0.8B model:
from nexora.ocr import NexoraOCR
ocr = NexoraOCR()
result = ocr.read("image.png")
print(result.text)
2B Model
To use the 2B model:
from nexora.ocr import NexoraOCR
ocr = NexoraOCR(model="nexora-ocr-v0.1-2b")
result = ocr.read("image.png")
print(result.text)
Available Models
You can retrieve the currently supported OCR models without loading a model:
from nexora.ocr import NexoraOCR
models = NexoraOCR.available_models()
print(models)
Available models currently include:
nexora-ocr-v0.1-0.8b
nexora-ocr-v0.1-2b
The model names are resolved internally to their corresponding model repositories.
OCR
The OCR module provides an interface for extracting text from images using Nexora OCR models.
from nexora.ocr import NexoraOCR
ocr = NexoraOCR(model="nexora-ocr-v0.1-2b")
result = ocr.read("image.png")
print(result.text)
The API is designed to keep OCR inference simple while allowing the underlying models and implementation to evolve independently.
Device Selection
Nexora supports automatic device selection as well as explicit device configuration.
from nexora.ocr import NexoraOCR
ocr = NexoraOCR(
model="nexora-ocr-v0.1-2b",
device="auto",
)
result = ocr.read("image.png")
print(result.text)
The default device configuration is auto.
Models
The currently supported OCR models are:
| Model | Hugging Face Repository | Size |
|---|---|---|
nexora-ocr-v0.1-0.8b |
ArkAiLab-Adl/nexora-ocr-v0.1-0.8b |
0.8B |
nexora-ocr-v0.1-2b |
ArkAiLab-Adl/nexora-ocr-v0.1-2b |
2B |
The models are distributed separately from the Python package through the Hugging Face Hub.
Nexora handles model loading and inference through its internal model interface, keeping model weights separate from the Python distribution.
Project Structure
Nexora uses a modular architecture so different AI capabilities can be integrated through dedicated modules.
nexora/
├── src/
│ └── nexora/
│ ├── __init__.py
│ │
│ └── ocr/
│ ├── __init__.py
│ ├── ocr.py
│ ├── model.py
│ ├── processor.py
│ └── result.py
│
├── tests/
├── README.md
├── LICENSE
├── requirements.txt
└── pyproject.toml
Requirements
Nexora currently relies on the following core libraries:
- PyTorch
- Transformers
- Hugging Face Hub
- Accelerate
- SafeTensors
- Pillow
- NumPy
Python 3.10 or newer is required.
Development
Clone the repository:
git clone https://github.com/ArkDevelopmentLabs/nexora
cd nexora
Create a virtual environment:
python -m venv .venv
Activate it on Windows:
.\.venv\Scripts\Activate.ps1
Install the development dependencies:
pip install -r requirements.txt
Install Nexora in editable mode:
pip install -e .
Roadmap
Nexora is being developed as a broader AI toolkit.
Planned improvements include:
- Improved OCR inference
- Batch inference
- GPU and CPU optimization
- Additional model integrations
- Model management utilities
- Additional computer vision capabilities
- Additional machine learning capabilities
- Improved inference and deployment utilities
The API and implementation details may evolve as the project develops.
License
Nexora is licensed under the Apache License 2.0.
See the LICENSE file for the complete license text.
Project
Nexora is developed by ArkDevLabs.
GitHub:
https://github.com/ArkDevelopmentLabs/nexora
PyPI:
https://pypi.org/project/nexora-ai/
Status
Nexora is currently in alpha development.
The 0.1.x releases are intended for experimentation, development, and early testing. APIs and implementation details may change between releases.
Metadata
Release files for nexora-ai 0.2.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| nexora_ai-0.2.1.tar.gz | 11.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| nexora_ai-0.2.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 21.9 kB
Release files / nexora_ai-0.2.1.tar.gz
| Download URL | nexora_ai-0.2.1.tar.gz |
|---|---|
| Size | 11.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
e0613e66fecee440de1ae34110c7f7ff6ad812cb5332b707773a1e1d82c4c419
|
|
BLAKE2b-256 checksum How to use checksums |
4773a54ce90eaa9d49ef021054fb8f7bee2c987b4a64a4d704069a8fa627e3bd
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.13
|
Release files / nexora_ai-0.2.1-py3-none-any.whl
| Download URL | nexora_ai-0.2.1-py3-none-any.whl |
|---|---|
| Size | 10.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
85dc16575bb4bc70917a946bc190187fd80a2dc87652c90e402c4f1ee1802c0f
|
|
BLAKE2b-256 checksum How to use checksums |
84fc7aa29ec7b7a66fdfeb79cab13b8c08e66c1fb76119a4b2e296e477015c22
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.13
|