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Nexora

Status: ExperimentalBase Model: Qwen3.5 0.8BTask: OCR / Text ExtractionVersion: v0.1

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

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result)

Nexora automatically loads the configured model and performs inference using the provided image.

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

OCR

The OCR module provides an interface for extracting text from images using Nexora OCR models.

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result.text)

The API is designed to keep OCR inference simple while allowing the underlying model and implementation to evolve independently.

Model

The initial OCR implementation uses:

ArkAiLab-Adl/nexora-ocr-v0.1-0.8b

The model is based on the Qwen3.5-0.8B architecture and is distributed through the Hugging Face Hub.

Nexora keeps model weights separate from the Python distribution and handles model loading and inference through its internal model interface.

Requirements

Nexora currently relies on the following core libraries:

  • PyTorch
  • Transformers
  • Hugging Face Hub
  • Accelerate
  • SafeTensors
  • Pillow
  • NumPy

Python 3.10 or newer is recommended.

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.

About

No description, website, or topics provided.

Resources

Stars

2 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
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Nexora

Status: ExperimentalBase Model: Qwen3.5 0.8BTask: OCR / Text ExtractionVersion: v0.1

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

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result)

Nexora automatically loads the configured model and performs inference using the provided image.

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

OCR

The OCR module provides an interface for extracting text from images using Nexora OCR models.

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result.text)

The API is designed to keep OCR inference simple while allowing the underlying model and implementation to evolve independently.

Model

The initial OCR implementation uses:

ArkAiLab-Adl/nexora-ocr-v0.1-0.8b

The model is based on the Qwen3.5-0.8B architecture and is distributed through the Hugging Face Hub.

Nexora keeps model weights separate from the Python distribution and handles model loading and inference through its internal model interface.

Requirements

Nexora currently relies on the following core libraries:

  • PyTorch
  • Transformers
  • Hugging Face Hub
  • Accelerate
  • SafeTensors
  • Pillow
  • NumPy

Python 3.10 or newer is recommended.

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.

About

No description, website, or topics provided.

Resources

Stars

2 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Nexora

Status: ExperimentalBase Model: Qwen3.5 0.8BTask: OCR / Text ExtractionVersion: v0.1

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

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result)

Nexora automatically loads the configured model and performs inference using the provided image.

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

OCR

The OCR module provides an interface for extracting text from images using Nexora OCR models.

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result.text)

The API is designed to keep OCR inference simple while allowing the underlying model and implementation to evolve independently.

Model

The initial OCR implementation uses:

ArkAiLab-Adl/nexora-ocr-v0.1-0.8b

The model is based on the Qwen3.5-0.8B architecture and is distributed through the Hugging Face Hub.

Nexora keeps model weights separate from the Python distribution and handles model loading and inference through its internal model interface.

Requirements

Nexora currently relies on the following core libraries:

  • PyTorch
  • Transformers
  • Hugging Face Hub
  • Accelerate
  • SafeTensors
  • Pillow
  • NumPy

Python 3.10 or newer is recommended.

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.

About

No description, website, or topics provided.

Resources

Stars

2 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

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Nexora

Status: ExperimentalBase Model: Qwen3.5 0.8BTask: OCR / Text ExtractionVersion: v0.1

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

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result)

Nexora automatically loads the configured model and performs inference using the provided image.

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

OCR

The OCR module provides an interface for extracting text from images using Nexora OCR models.

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result.text)

The API is designed to keep OCR inference simple while allowing the underlying model and implementation to evolve independently.

Model

The initial OCR implementation uses:

ArkAiLab-Adl/nexora-ocr-v0.1-0.8b

The model is based on the Qwen3.5-0.8B architecture and is distributed through the Hugging Face Hub.

Nexora keeps model weights separate from the Python distribution and handles model loading and inference through its internal model interface.

Requirements

Nexora currently relies on the following core libraries:

  • PyTorch
  • Transformers
  • Hugging Face Hub
  • Accelerate
  • SafeTensors
  • Pillow
  • NumPy

Python 3.10 or newer is recommended.

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.

About

No description, website, or topics provided.

Resources

Stars

2 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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Nexora

Status: ExperimentalBase Model: Qwen3.5 0.8BTask: OCR / Text ExtractionVersion: v0.1

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

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result)

Nexora automatically loads the configured model and performs inference using the provided image.

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

OCR

The OCR module provides an interface for extracting text from images using Nexora OCR models.

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result.text)

The API is designed to keep OCR inference simple while allowing the underlying model and implementation to evolve independently.

Model

The initial OCR implementation uses:

ArkAiLab-Adl/nexora-ocr-v0.1-0.8b

The model is based on the Qwen3.5-0.8B architecture and is distributed through the Hugging Face Hub.

Nexora keeps model weights separate from the Python distribution and handles model loading and inference through its internal model interface.

Requirements

Nexora currently relies on the following core libraries:

  • PyTorch
  • Transformers
  • Hugging Face Hub
  • Accelerate
  • SafeTensors
  • Pillow
  • NumPy

Python 3.10 or newer is recommended.

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.

About

No description, website, or topics provided.

Resources

Stars

2 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Nexora

Status: ExperimentalBase Model: Qwen3.5 0.8BTask: OCR / Text ExtractionVersion: v0.1

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

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result)

Nexora automatically loads the configured model and performs inference using the provided image.

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

OCR

The OCR module provides an interface for extracting text from images using Nexora OCR models.

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result.text)

The API is designed to keep OCR inference simple while allowing the underlying model and implementation to evolve independently.

Model

The initial OCR implementation uses:

ArkAiLab-Adl/nexora-ocr-v0.1-0.8b

The model is based on the Qwen3.5-0.8B architecture and is distributed through the Hugging Face Hub.

Nexora keeps model weights separate from the Python distribution and handles model loading and inference through its internal model interface.

Requirements

Nexora currently relies on the following core libraries:

  • PyTorch
  • Transformers
  • Hugging Face Hub
  • Accelerate
  • SafeTensors
  • Pillow
  • NumPy

Python 3.10 or newer is recommended.

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.

About

No description, website, or topics provided.

Resources

Stars

2 stars

Watchers

0 watching

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Releases

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Used by

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Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Nexora

Status: ExperimentalBase Model: Qwen3.5 0.8BTask: OCR / Text ExtractionVersion: v0.1

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

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result)

Nexora automatically loads the configured model and performs inference using the provided image.

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

OCR

The OCR module provides an interface for extracting text from images using Nexora OCR models.

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result.text)

The API is designed to keep OCR inference simple while allowing the underlying model and implementation to evolve independently.

Model

The initial OCR implementation uses:

ArkAiLab-Adl/nexora-ocr-v0.1-0.8b

The model is based on the Qwen3.5-0.8B architecture and is distributed through the Hugging Face Hub.

Nexora keeps model weights separate from the Python distribution and handles model loading and inference through its internal model interface.

Requirements

Nexora currently relies on the following core libraries:

  • PyTorch
  • Transformers
  • Hugging Face Hub
  • Accelerate
  • SafeTensors
  • Pillow
  • NumPy

Python 3.10 or newer is recommended.

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.

About

No description, website, or topics provided.

Resources

Stars

2 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
Skip to content

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1 Commit

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Nexora

Status: ExperimentalBase Model: Qwen3.5 0.8BTask: OCR / Text ExtractionVersion: v0.1

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

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result)

Nexora automatically loads the configured model and performs inference using the provided image.

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

OCR

The OCR module provides an interface for extracting text from images using Nexora OCR models.

fromnexora.ocrimportNexoraOCRocr=NexoraOCR()
result=ocr.read("image.png")
print(result.text)

The API is designed to keep OCR inference simple while allowing the underlying model and implementation to evolve independently.

Model

The initial OCR implementation uses:

ArkAiLab-Adl/nexora-ocr-v0.1-0.8b

The model is based on the Qwen3.5-0.8B architecture and is distributed through the Hugging Face Hub.

Nexora keeps model weights separate from the Python distribution and handles model loading and inference through its internal model interface.

Requirements

Nexora currently relies on the following core libraries:

  • PyTorch
  • Transformers
  • Hugging Face Hub
  • Accelerate
  • SafeTensors
  • Pillow
  • NumPy

Python 3.10 or newer is recommended.

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.

About

No description, website, or topics provided.

Resources

Stars

2 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

Contributors

Languages