QuantumLingua Specification

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QuantumLingua

Language for the People, Accelerated.

QuantumLingua is an open-source AI translation and multilingual language-processing platform designed around Model Compression and Parallelization (MCP). It combines model optimization, parallel inference, multilingual processing, multimodal translation, privacy-preserving deployment, and extensible language tooling into a modular architecture.

The system is designed to make advanced translation faster, more efficient, more accessible, and deployable across cloud infrastructure, local computers, mobile devices, edge hardware, and distributed environments.

Project Goals

QuantumLingua is designed to:

Reduce translation inference latency
Reduce model memory requirements
Increase translation throughput
Preserve translation quality after optimization
Support multilingual and cross-language workflows
Support text, audio, images, video, and other multimodal inputs
Enable offline and local-first translation
Provide scalable CPU, GPU, and distributed execution
Support rare, regional, minority, and endangered languages
Provide developer, research, educational, and enterprise tooling
Maintain modularity and avoid unnecessary vendor lock-in
Allow capabilities to be added through optional plugins
Provide reproducible performance and quality benchmarking
Give users control over privacy, models, terminology, and translation behavior

Architecture

QuantumLingua uses a modular architecture in which the core system provides the essential translation and optimization infrastructure while optional plugin modules provide specialized capabilities.

Core Architecture

The platform consists of:

Model Compression
Parallelization Engine
Translation Engine
Language Intelligence
Context Management
Terminology and Translation Memory
Multilingual Language Registry
Multimodal Processing
Streaming Translation
Speech Processing
Document Processing
Quality Assurance
Benchmarking
Model Registry
Dataset Management
Privacy and Security
Local and Edge Runtime
Distributed Runtime
API and CLI
Web Interface
Personalization
Accessibility
Research and Experimentation
Collaboration
Administration and Governance

Optional capabilities are implemented through a plugin architecture.

Modular Design Principles

QuantumLingua modules must be:

Independently maintainable
Loosely coupled
Replaceable
Testable
Observable
Versioned
Hardware-aware
Backend-independent where practical
Compatible with local deployment
Compatible with distributed deployment
Extensible through stable interfaces

Core modules should contain capabilities required for the fundamental QuantumLingua platform.

Specialized capabilities that are useful but not required for the base translation engine should be implemented as optional plugins.

No module should require a proprietary cloud service to operate unless that integration is explicitly provided as an optional plugin.

Core Modules

Model Compression Module

The Model Compression Module reduces model size and computational requirements while attempting to preserve translation quality.

Supported compression strategies include:

Weight pruning
Structured pruning
Unstructured pruning
Quantization
Mixed-precision inference
Weight-only quantization
Activation quantization
Knowledge distillation
Low-rank factorization
Matrix decomposition
Parameter sharing
Vocabulary optimization
Layer reduction
Attention optimization
Redundant component removal
Model sparsification
Compression-aware fine-tuning

The module must provide:

Compression configuration
Compression profiles
Before-and-after model measurements
Memory measurements
Parameter counts
Model-size measurements
Accuracy comparison
Quality degradation detection
Compression reproducibility
Hardware-aware optimization
Reversible optimization workflows where supported

Compression must never be represented as automatically improving translation quality. The system must measure the actual quality and performance impact of each optimization.

Parallelization Engine

The Parallelization Engine distributes translation computation to reduce latency and increase throughput.

Supported strategies include:

Batch parallelism
Data parallelism
Model parallelism
Tensor parallelism
Pipeline parallelism
Sequence parallelism
Multi-threaded CPU execution
Multi-process execution
Multi-GPU execution
Multi-device execution
Distributed inference

The engine should automatically determine suitable execution strategies based on:

Available CPU cores
Available GPU resources
Available memory
Model architecture
Input size
Batch size
Translation direction
Latency requirements
Throughput requirements
Hardware topology

The system should support both low-latency single-request translation and high-throughput batch processing.

Translation Engine

The Translation Engine provides the primary translation pipeline.

It must support:

Source-language detection
Target-language selection
Sentence translation
Paragraph translation
Document translation
Batch translation
Streaming translation
Context-aware translation
Translation memory integration
Terminology integration
Domain-specific translation
Translation confidence estimation
Translation alternatives
Translation revision

The engine should separate:

Input normalization
Language identification
Context preparation
Translation inference
Post-processing
Quality validation
Output formatting

This separation allows individual stages to be optimized independently.

Language Intelligence Module

The Language Intelligence Module manages linguistic analysis before, during, and after translation.

Capabilities include:

Language identification
Dialect identification
Sentence segmentation
Tokenization
Morphological analysis
Named entity recognition
Semantic analysis
Grammar analysis
Terminology detection
Phrase detection
Context analysis
Ambiguity detection
Pronoun resolution
Reference resolution
Style detection
Formality detection
Sentiment detection
Emotion detection

The module should provide structured linguistic metadata to the Translation Engine.

Context Management Module

The Context Management Module maintains information required for context-aware translation.

It must support:

Sentence context
Paragraph context
Document context
Conversation context
Speaker context
Terminology context
Style context
Translation history
Cross-sentence references
Long-context management

The module should use efficient context windows and caching mechanisms to minimize unnecessary inference.

Terminology and Translation Memory Module

This module provides consistent terminology across translations.

Capabilities include:

Custom glossaries
Domain terminology
Translation memory
Phrase memory
Preferred translations
Forbidden translations
Regional terminology
Brand terminology
Technical terminology
Legal terminology
Scientific terminology
User-defined terminology

The system should support terminology priority levels and conflict resolution.

Multilingual Language Registry

The Language Registry manages supported languages and language variants.

Each language profile should define:

Language identifier
Language name
Script
Dialects
Regional variants
Tokenization requirements
Normalization rules
Supported translation directions
Available models
Available speech models
Available OCR models
Quality benchmarks
Dataset metadata

The architecture must allow additional languages to be added without modifying the core translation engine.

Multimodal Processing Module

The Multimodal Processing Module provides processing for non-text translation inputs.

Core support may include:

Image text extraction
OCR integration
Document image processing
Subtitle extraction
Structured text extraction
Visual text localization
Multimodal input normalization

The module should convert supported input into a normalized representation that can enter the standard translation pipeline.

Advanced multimodal capabilities may be provided through plugins.

Streaming Translation Module

The Streaming Translation Module provides low-latency translation for continuously arriving content.

It must support:

Incremental text
Streaming audio
Live captions
Partial translations
Translation buffering
Low-latency output
Segment revision
Context preservation

The system should prioritize responsiveness while allowing later segments to revise earlier provisional translations when additional context changes interpretation.

Speech Processing Module

The Speech Processing Module provides the speech pipeline required for spoken-language translation.

Core capabilities include:

Speech input handling
Speech segmentation
Speech-to-text integration
Language identification
Spoken-language translation
Timestamp preservation
Speaker segmentation
Caption generation

Speech recognition and synthesis engines should be replaceable.

Document Processing Module

The Document Processing Module handles structured and unstructured documents.

Supported workflows include:

Plain text
Markdown
HTML
PDF text extraction
Word-processing documents
Structured documents
Subtitle files
CSV and tabular text
Large-document streaming

The module must preserve document structure whenever possible.

Examples include:

Headings
Paragraphs
Tables
Lists
Metadata
Formatting markers
Links
Captions

Quality Assurance Module

The Quality Assurance Module evaluates translation quality and detects potential problems.

Capabilities include:

Automated quality scoring
Translation confidence
Semantic similarity
Terminology compliance
Grammar validation
Missing-content detection
Added-content detection
Contradiction detection
Numeric consistency
Date consistency
Entity consistency
Formatting consistency
Source-target alignment

The module should flag uncertain translations for human review.

Benchmarking Module

The Benchmarking Module measures performance and quality.

Metrics should include:

Translation latency
Tokens per second
Sentences per second
Documents per minute
Throughput
CPU utilization
GPU utilization
Memory consumption
Model size
Startup time
Warm inference time
Cold inference time
Batch performance
Streaming latency
Energy consumption where measurable

Translation quality metrics may include:

BLEU
ROUGE
METEOR
chrF
COMET
BERTScore
Semantic similarity
Human evaluation

Benchmarks must distinguish between:

Baseline model
Compressed model
Parallelized model
Compressed and parallelized model

QuantumLingua must not claim a specific speed improvement without a reproducible benchmark.

Model Registry Module

The Model Registry manages translation models and optimized model variants.

Each model record should contain:

Model identifier
Version
Architecture
Source language
Target language
Supported languages
Model size
Parameter count
Precision
Compression method
Quantization format
Hardware requirements
License
Source
Benchmark results
Integrity information
Compatibility information

The registry must preserve model licensing information.

Dataset Management Module

The Dataset Management Module manages multilingual datasets used for training, evaluation, and experimentation.

Capabilities include:

Dataset ingestion
Dataset validation
Language identification
Duplicate detection
Quality filtering
PII detection
Alignment validation
Dataset versioning
Dataset provenance
Dataset licensing
Train/validation/test splitting
Dataset statistics

Datasets must retain applicable licensing and attribution information.

Privacy and Security Module

The Privacy and Security Module provides privacy-preserving operation.

Capabilities include:

Local-only processing
Offline operation
Encryption
Secure API communication
Access controls
Authentication
Authorization
Audit logging
Data retention controls
Data deletion
Sensitive-content handling
Model integrity verification
Dependency integrity verification

Translation content should not be transmitted to external services unless the user explicitly enables an external integration.

Local and Edge Runtime Module

The Local and Edge Runtime enables QuantumLingua to operate without centralized cloud infrastructure.

Supported targets may include:

Desktop computers
Laptops
Mobile devices
Edge servers
Single-board computers
Local GPU systems
CPU-only systems

The runtime should automatically select an appropriate model and optimization profile based on available resources.

Distributed Runtime Module

The Distributed Runtime provides scalable execution across multiple machines.

Capabilities include:

Distributed inference
Worker discovery
Job scheduling
Load balancing
Fault handling
Queue management
Model distribution
Result aggregation
Resource-aware scheduling

The system should support both homogeneous and heterogeneous hardware.

API Module

The API Module provides programmatic access to QuantumLingua.

API capabilities should include:

Translation
Language detection
Batch translation
Streaming translation
Model discovery
Benchmark execution
Glossary management
Translation memory
Health checks
Runtime information

The API should provide versioned interfaces.

CLI Module

The CLI provides command-line access to core QuantumLingua functionality.

CLI operations should include:

Translate
Detect language
List models
Download models
Compress models
Benchmark models
Run optimization
Start local services
Manage glossaries
Manage translation memory
Inspect hardware
Inspect runtime configuration

Web Interface Module

The Web Interface provides a user-friendly interface for translation and system management.

Core functionality should include:

Text translation
Language selection
Automatic language detection
Translation history
Glossaries
Translation memory
Model selection
Performance information
Privacy controls
Offline/local status
Accessibility controls

The interface should remain usable without requiring users to understand model architecture.

Personalization Module

The Personalization Module allows users to define translation preferences.

Capabilities include:

Preferred terminology
Writing style
Formality
Regional language preferences
Translation history
Glossaries
Preferred models
Output formatting
Correction preferences

Personalization data should remain local by default.

Accessibility Module

The Accessibility Module provides inclusive interfaces and workflows.

Capabilities include:

Screen-reader compatibility
Keyboard navigation
Adjustable text size
High-contrast support
Voice input
Speech output
Caption support
Simplified interfaces
Offline operation
Support for underserved language communities

Research and Experimentation Module

The Research Module provides tools for experimentation.

Capabilities include:

Model comparisons
Compression experiments
Quantization experiments
Parallelization experiments
Architecture comparisons
Dataset experiments
Benchmark automation
Experiment tracking
Reproducibility records
Ablation studies
Optimization analysis

Research results should record configuration, hardware, models, datasets, and metrics.

Collaboration Module

The Collaboration Module supports multilingual collaborative workflows.

Capabilities include:

Shared translation projects
Collaborative documents
Shared glossaries
Shared translation memory
Translation review
Human corrections
Approval workflows
Version history
Contributor attribution

Administration and Governance Module

The Administration Module manages system configuration and governance.

Capabilities include:

User management
Roles
Permissions
API credentials
Model permissions
Plugin permissions
Usage policies
Audit logs
System configuration
Resource quotas
Deployment configuration

Optional Plugin Modules

Optional plugins extend QuantumLingua without increasing the requirements of the core platform.

Speech Synthesis Plugin

Provides text-to-speech functionality.

Possible capabilities:

Multilingual speech synthesis
Voice selection
Voice cloning where legally permitted
Speaking-style control
Speed control
Audio export
Streaming speech

Advanced OCR Plugin

Provides specialized OCR capabilities.

Possible capabilities:

Handwriting recognition
Historical document recognition
Complex-layout OCR
Table recognition
Form recognition
Multi-script recognition

Video Translation Plugin

Provides video translation workflows.

Capabilities may include:

Speech extraction
Subtitle extraction
Translation
Subtitle generation
Caption timing
Speaker identification
Translated audio generation

Live Meeting Translation Plugin

Provides real-time translation for meetings.

Capabilities may include:

Live transcription
Speaker identification
Real-time translation
Live captions
Translation overlays
Meeting transcripts
Exportable translated records

Browser Translation Plugin

Provides translation directly within web browsers.

Capabilities may include:

Page translation
Selected-text translation
Automatic language detection
Local translation
Translation memory
Custom terminology

IDE Translation Plugin

Provides multilingual assistance inside development environments.

Capabilities may include:

Code comment translation
Documentation translation
README translation
Localization assistance
Developer communication translation

Messaging Translation Plugin

Provides real-time translation for messaging systems.

Capabilities may include:

Incoming message translation
Outgoing message translation
Conversation context
User language profiles
Shared terminology

Collaboration Platform Plugin

Integrates QuantumLingua with external collaboration systems.

Potential integrations include:

Team chat
Project management
Documentation platforms
Knowledge bases
Customer support systems

Enterprise Workflow Plugin

Provides integrations for organizational workflows.

Capabilities may include:

CRM integration
Document management
Internal knowledge bases
Customer support
Enterprise translation workflows
Private model deployment

Domain Model Plugin

Provides specialized models for specific industries.

Potential domains include:

Legal
Finance
Engineering
Scientific research
Education
Manufacturing
Government
Technical documentation

Language Expansion Plugin

Provides additional languages and language variants without changing the core architecture.

Plugins may include:

Language models
Tokenizers
Linguistic rules
Datasets
Evaluation sets
Terminology resources

Sign Language Plugin

Provides sign-language recognition and translation capabilities.

Potential workflows include:

Video input
Gesture recognition
Sign-language-to-text
Sign-language-to-speech
Text-to-sign-language visualization

Augmented Reality Translation Plugin

Provides visual translation overlays for supported AR devices.

Capabilities may include:

Camera input
Text detection
Translation
Spatial text placement
Real-time overlays

Language Learning Plugin

Provides educational language-learning workflows.

Capabilities include:

Vocabulary extraction
Grammar explanations
Pronunciation assistance
Side-by-side translations
Practice exercises
Interactive conversation
Translation comparison

Semantic Search Plugin

Provides cross-language search.

Capabilities include:

Cross-language document search
Semantic retrieval
Multilingual embeddings
Query translation
Cross-language ranking
Knowledge-base search

Multilingual Knowledge Plugin

Provides multilingual knowledge representation.

Capabilities may include:

Cross-language entity mapping
Concept alignment
Knowledge graph integration
Multilingual semantic relationships
Cross-language reasoning support

Community Model Marketplace Plugin

Provides optional model sharing infrastructure.

Capabilities may include:

Community model publishing
Model discovery
Model versioning
Benchmark submission
License metadata
Attribution metadata
Model reputation

Any marketplace implementation must preserve the licenses and attribution requirements of individual models.

Community Contribution Plugin

Provides structured community participation.

Capabilities may include:

Translation corrections
Language contributions
Dataset contributions
Glossary contributions
Benchmark submissions
Contributor profiles
Contribution history

Competition and Benchmark Leaderboard Plugin

Provides optional public performance rankings.

Possible rankings include:

Fastest model
Lowest memory usage
Best translation quality
Best quality-to-compute ratio
Best edge performance
Best language coverage

Leaderboards must disclose hardware and benchmark configurations.

Gamification Plugin

Provides optional contributor recognition.

Possible features include:

Badges
Contribution milestones
Challenges
Achievement records
Community rankings

Gamification must remain optional and must not affect core translation functionality.

Donation and Sponsorship Plugin

Provides optional project sustainability mechanisms.

Possible capabilities include:

Donations
Sponsorships
Funding campaigns
Contributor support

The core platform must remain functional without the plugin.

Translation Pipeline

The standard translation pipeline should follow this logical sequence:

Input

Language Detection

Input Normalization

Context Preparation

Terminology Retrieval

Model Selection

Model Optimization

Compression

Parallelization

Translation Inference

Post-Processing

Quality Validation

Formatting Restoration

Output

The pipeline must permit individual stages to be replaced or optimized without rewriting unrelated modules.

Performance Optimization

QuantumLingua should optimize for:

Latency
Throughput
Memory efficiency
Model size
Startup time
Hardware utilization
Energy efficiency
Network efficiency
Translation quality

Optimization must be evidence-based.

The project should never guarantee fixed performance multipliers because actual results depend on:

Model architecture
Hardware
Quantization method
Input length
Batch size
Language pair
Runtime
Backend
Parallelization strategy

Dynamic Optimization

QuantumLingua should support runtime optimization profiles.

Profiles may include:

Maximum speed
Balanced
Maximum quality
Minimum memory
CPU-only
GPU
Edge
Mobile
Offline
Batch throughput
Real-time latency

The runtime should select an appropriate configuration automatically when automatic optimization is enabled.

Translation Quality Protection

Optimization must not be performed solely for speed.

Every optimization workflow should provide:

Baseline quality
Optimized quality
Quality delta
Baseline latency
Optimized latency
Memory delta
Throughput delta

Users should be able to establish acceptable quality thresholds.

The system should reject or flag optimization configurations that exceed configured quality degradation limits.

Human Review

QuantumLingua should support human review for uncertain translations.

Review workflows may include:

Confidence-based review
Terminology violations
Semantic inconsistencies
Missing content
Added content
Ambiguous translations
Domain-specific terminology
Low-confidence language detection

Human corrections should be optionally stored in translation memory or approved datasets.

Translation Memory

Translation memory should support:

Exact matches
Fuzzy matches
Semantic matches
Language-pair matching
Domain matching
User-specific memory
Project-specific memory
Organization-specific memory

Users must control whether translation memory is stored and reused.

Adaptive Learning

Adaptive learning should be designed around explicit user control.

Possible inputs include:

Approved corrections
Translation memory
Glossaries
Domain terminology
Style preferences

The system must not silently use private user content as training data.

Privacy Model

The default privacy model should favor local processing.

The system should clearly identify:

Local processing
Remote processing
External model providers
External API calls
Data retention
Data storage
Logging behavior

Users must be able to disable external processing when supported.

Security Requirements

Security controls should include:

Dependency verification
Model integrity verification
Secure model downloads
Authentication
Authorization
Encrypted communication
Secure credential handling
Input validation
Output validation
Audit logging
Plugin permission controls
Resource limits

Plugins should operate within defined permission boundaries where technically possible.

Data Provenance

QuantumLingua should track provenance for:

Models
Datasets
Translation memories
Glossaries
Plugins
Benchmark results
User corrections
Generated outputs where required

Provenance records should identify applicable source, version, license, and attribution information.

Installation Requirements

A production implementation should provide documented installation paths for:

CPU-only systems
NVIDIA GPU systems
Other supported accelerators
Local servers
Cloud servers
Edge devices
Containers
Development environments

The implementation should clearly identify optional dependencies for optional capabilities.

Configuration

QuantumLingua configuration should support:

Default language
Target language
Model
Compression profile
Quantization
Parallelization strategy
Batch size
Context size
Memory limits
Quality thresholds
Privacy settings
Logging
Plugin activation
Hardware selection
Cache settings
Translation memory
Glossaries

Configuration should support environment variables and configuration files where appropriate.

Testing

The project should provide tests for:

Translation correctness
Language detection
Context preservation
Terminology enforcement
Compression
Quantization
Parallelization
Model loading
API behavior
CLI behavior
Streaming
Document processing
Privacy controls
Plugin interfaces
Error handling

Performance tests should be reproducible.

Benchmarking Requirements

Every significant performance optimization should include benchmark results.

Benchmarks should record:

Hardware
Operating system
Runtime
Model
Model version
Model precision
Compression method
Parallelization method
Input size
Batch size
Language pair
Latency
Throughput
Memory usage
Translation quality

Benchmark results should not be generalized beyond the tested configuration without supporting evidence.

Error Handling

The system should provide structured errors for:

Unsupported languages
Missing models
Invalid model formats
Insufficient memory
Unsupported hardware
Invalid configuration
Plugin failures
Network failures
Dataset failures
Translation failures

Errors should provide actionable information without exposing sensitive data.

Observability

QuantumLingua should provide optional observability for:

Request latency
Translation throughput
Model loading time
Memory usage
Hardware utilization
Queue depth
Parallel worker status
Plugin status
Error rates
Quality metrics

Sensitive translation content must not be logged by default.

Scalability

The architecture should scale from:

Single sentence translation
Individual documents
Large document collections
Real-time conversations
Batch translation jobs
Multi-user services
Distributed translation clusters

The same core translation abstractions should be usable across deployment sizes.

Offline Operation

Offline operation should allow users to:

Install models locally
Translate without internet access
Use local glossaries
Use local translation memory
Run local benchmarks
Manage local model versions
Disable external network access

Offline mode should not require external authentication or remote APIs.

Accessibility and Global Language Support

QuantumLingua should prioritize languages and communities that may be underserved by commercial translation systems.

The architecture should make it possible for communities to contribute:

Language resources
Datasets
Terminology
Evaluation datasets
Translation memories
Language-specific processing
Documentation

Community contributions must preserve applicable licensing and attribution requirements.

Development Roadmap

Foundation

Establish modular architecture
Implement model loading
Implement translation pipeline
Implement language registry
Implement basic compression
Implement basic parallelization
Implement CLI
Implement API
Establish benchmark framework

Performance

Add advanced quantization
Add structured pruning
Add knowledge distillation workflows
Add hardware detection
Add dynamic optimization
Add multi-device execution
Add distributed execution
Add memory optimization

Language Intelligence

Expand language coverage
Add context management
Add terminology management
Add translation memory
Add quality validation
Add adaptive translation workflows

Multimodal

Add OCR
Add document processing
Add streaming speech
Add subtitle translation
Add audio translation
Add multimodal workflows

Ecosystem

Add plugin framework
Add model registry
Add dataset management
Add research tools
Add web interface
Add collaboration tools
Add community contribution infrastructure

Advanced Capabilities

Add optional semantic search
Add optional multilingual knowledge systems
Add optional AR translation
Add optional sign-language processing
Add optional live meeting translation
Add optional language-learning tools
Add optional enterprise integrations

Plugin Architecture

Plugins should use defined interfaces rather than directly modifying core internals.

A plugin should declare:

Plugin name
Plugin version
QuantumLingua compatibility
Capabilities
Dependencies
License
Attribution
Required permissions
Configuration options

The plugin system should support:

Installation
Removal
Enablement
Disablement
Version management
Dependency management
Capability discovery
Permission management

A failed optional plugin must not prevent the core translation engine from operating unless the user explicitly depends on that plugin.

API Design

The API should expose stable abstractions rather than implementation-specific internals.

Primary API categories should include:

Translation
Language detection
Models
Compression
Parallelization
Benchmarks
Glossaries
Translation memory
Documents
Streaming
Plugins
Runtime status

API versions should be explicitly managed.

CLI Design

The CLI should provide intuitive commands for:

Translation
Model management
Compression
Optimization
Benchmarking
Language management
Glossary management
Translation memory
Plugin management
Runtime inspection
Configuration

CLI output should support both human-readable and machine-readable formats.

Model Compatibility

The architecture should remain model-agnostic wherever practical.

Model adapters should isolate model-specific implementation details from the core translation engine.

Adapters may support:

Encoder-decoder models
Decoder-based models
Multilingual models
Specialized translation models
Compressed models
Quantized models
Edge-optimized models

Hardware Optimization

The runtime should detect available hardware and determine appropriate execution strategies.

Hardware profiles may include:

CPU
GPU
NPU
TPU
Mobile accelerator
Edge accelerator

Optimization should account for:

Compute capability
Memory capacity
Memory bandwidth
Parallel execution capability
Precision support
Power constraints

Caching

QuantumLingua should support caching for:

Models
Tokenizers
Translation memory
Glossaries
Context
Repeated translations
Benchmark data

Caches should be configurable and privacy-aware.

Internationalization

The QuantumLingua interface itself should support localization.

Localization should include:

Interface text
Documentation
Error messages
CLI messages
Accessibility text
Help content

The translation platform should be capable of translating its own interface while preserving technical terminology.

Documentation Requirements

Documentation should include:

Installation
Configuration
Architecture
Core modules
Plugin development
Model integration
Compression
Parallelization
Benchmarking
Translation workflows
API usage
CLI usage
Deployment
Security
Privacy
Contribution guidelines
Licensing

Technical documentation should distinguish implemented functionality from planned functionality.

Contribution Requirements

Contributors should:

Follow the modular architecture
Include tests for new functionality
Include documentation for public interfaces
Include benchmarks for performance changes
Preserve licensing information
Preserve required attribution
Respect third-party licenses
Avoid introducing unnecessary vendor dependencies

See contributing.md for contribution requirements.

Project Vision

QuantumLingua aims to make high-performance multilingual AI more accessible by combining efficient model engineering with open-source collaboration.

The long-term vision is a translation platform that can operate anywhere, from a low-power local device to a distributed computing cluster, while giving users control over models, data, terminology, privacy, and performance.

QuantumLingua is designed around a simple principle:

Language for the People, Accelerated.

Specification Branding License (SBL)

Standard

Fully AGPL-3.0+ compliant system
Copyleft enforced for network deployments
Required attribution:

Roxanne Ardary
https://www.roxanneardary.com/

Optional

Specification Branding License (SBL)
Attribution-free commercial deployment
Pricing based on scale, usage, and deployment scope
https://roxanneardary.com/quantumlingua/

License & Notice Requirements

QuantumLingua is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).
By contributing to this project, you agree that your contributions will also be released under this license.

Please note the following:

All contributions must comply with the AGPL-3.0+ terms.
Under Section 7 of the license, all redistributions, forks, and derivative works must preserve attribution to:
Roxanne Ardary and roxanneardary.com.
QuantumLingua specifications are free to use with attribution. A Specification Branding License can be negotiated upon request.
The project’s notice.md file tracks attribution requirements and contributor acknowledgments.
Any update that adds new contributors or modifies attribution should also update notice.md.
When submitting a pull request, ensure that any new files maintain the attribution headers where applicable.
Network-deployed versions of this software must also remain fully AGPL-3.0+ compliant, including exposure of source code modifications when applicable under the license.

For full legal details, please refer to the AGPL-3.0+ license and the project’s notice.md file.

Notice – QuantumLingua

Attribution Requirement: Under Section 7 of the AGPL 3.0+ license, all redistributions, forks, and derivative works, including network-deployed versions of this project, must provide attribution to Roxanne Ardary and roxanneardary.com.

Contributors

This file tracks contributors and their specific contributions to the project.

Roxanne Ardary, roxanneardary.com – March 28, 2026
Created the QuantumLingua repository and defined the core vision for an open-source, high-speed AI translation ecosystem powered by Model Compression and Parallelization (MCP).
[Add other contributors here] – [Date]
[Describe contribution in one sentence]

License – QuantumLingua

This repository is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

Key Points:

You are free to use, modify, and distribute the code.
All redistributions, forks, and derivative works or network-deployed versions must also be licensed under AGPL-3.0+ and provide attribution to Roxanne Ardary and roxanneardary.com as required under Section 7 of the license.
The software is provided “as is,” without warranty of any kind.

For the full license text, see GNU AGPL-3.0 License.