MGML
Enterprise AI with Continual Learning
Modulated Gradient Memory Learning technology for closed corporate AI. Trained and running inside your infrastructure with the highest level of data security. Continuous learning without forgetting.
What is MGML?
MGML (Modulated Gradient Memory Learning) is a revolutionary architecture that enables large language models to continuously learn from new data without catastrophic forgetting.
Unlike traditional AI models that require full retraining when new data arrives, MGML uses a cascaded memory system with multi-level learning frequencies. This allows the model to integrate new knowledge gradually while preserving previously acquired skills and knowledge.
Example: AI PSY HELP
AI PSY HELP is built on MGML technology, demonstrating how the system can be specialized for psychological support while maintaining general conversational abilities. The model learns from new therapeutic content and user feedback without losing its core counseling skills, ethical safeguards, or safety protocols.
Key Features
Continual Learning
Learn from new data continuously without forgetting previous knowledge. Only 1.98% of parameters are trainable, making updates fast and cost-effective.
On-Premises Deployment
Complete data sovereignty. Train, fine-tune, and run inference entirely within your infrastructure. No data leaves your network.
Highest Data Security
Enterprise-grade security with complete control over data flows, access logs, and audit trails. Suitable for regulated industries.
Multi-Level Memory System
Cascaded Memory Blocks (CMB) with 4 levels (L1-L4) that learn at different frequencies, from fast adaptation to long-term consolidation.
Cost-Effective Training
Training costs approximately €600 for 22 hours. Infrastructure costs around €7,000/month for production deployment with up to 100 requests per minute.
Domain-Agnostic Architecture
Works across multiple domains. Currently deployed for psychological support (MGML-Psy), with plans for code generation (MGML-Code) and other domains.
Architecture Overview
MGML combines cascaded memory blocks with hierarchical optimization for stable continual learning
Cascaded Memory Blocks (CMB)
Multi-level memory system embedded in transformer layers. Four levels (L1-L4) update at different frequencies: L1 every step (fast), L2 every 5 steps, L3 every 25 steps, L4 every 125 steps (long-term).
Hierarchical Momentum Optimizer (HMO)
Custom optimizer with dual momentum: fast momentum (β₁=0.9) for quick adaptation and slow momentum (β_slow=0.9999) for long-term stability. Combines both for balanced updates.
Parameter-Efficient Design
Built on Mixtral 8×7B with LoRA adapters. Only 139M parameters (1.98% of base model) are trainable, reducing computational costs while maintaining performance.
Base Model
Built on Mistral AI's Mixtral 8×7B-Instruct, a Mixture-of-Experts model with ~47B total parameters but only ~13B active per token.
Enterprise Benefits
Why choose MGML for your organization?
Complete Data Sovereignty
All training, fine-tuning, and inference happens within your infrastructure. No sensitive data is sent to external services.
Regulatory Compliance
Perfect for healthcare (HIPAA), finance (PCI-DSS), defense, and other regulated industries where data residency is mandatory.
Deep Customization
Learn company-specific terminology, internal processes, proprietary knowledge bases, and organizational culture. Impossible with external cloud APIs.
Operational Independence
No dependencies on external AI services, API rate limits, or service disruptions. Operates entirely within your corporate network.
Cost Predictability
Fixed infrastructure costs instead of variable API pricing. Better financial planning and no unexpected cost spikes.
Continuous Improvement
Update models with new data quarterly or on-demand. Each update takes ~22 hours and costs ~€600, making continuous improvement economically sustainable.
Enterprise Use Cases
MGML is designed for organizations that need secure, customizable AI
Healthcare & Medical AI
Clinical assistants that learn from new medical research and hospital protocols. All patient data stays on hospital servers, preserving confidentiality and HIPAA compliance.
Financial Services
Compliance AI that adapts to new regulations and fraud patterns. Learn from regulatory documents and internal data without exposing sensitive financial information.
Legal Research
Legal assistants that ingest new case law and statutes. Absorb new legal precedents without overwriting understanding of long-standing laws.
Manufacturing & Industry 4.0
Predictive maintenance and production optimization AI. Learn from new sensor data and incident reports while preserving known equipment patterns.
Government & Defense
Intelligence analysis and logistics AI. Learn from incoming reports and evolving scenarios while preserving historical context. Operates on secure government servers.
Retail & E-commerce
Personalized recommendation engines that adapt to seasonal trends and customer preferences. Learn from weekly sales data while remembering long-term patterns.
Technical Specifications
Production-ready infrastructure requirements
Training Infrastructure
2×A100 GPUs (80GB) for initial fine-tuning. Training time: ~22 hours. Cost: approximately €600 per training session.
Inference Infrastructure
Standard_NC48ads_A100_v4 instance (48 CPU cores, 440GB RAM, 1×A100 GPU). Operational cost: €7,000/month for 24/7 operation. Handles up to 100 requests per minute with sub-2-second response times.
Performance Metrics
95th percentile latency: ~1.8 seconds. Memory usage: ~70GB per GPU with 8-bit quantization. Successfully handles 10-50 concurrent users per instance.
Security Features
Complete control over security configurations, access controls, and audit trails. Can operate air-gapped if required. All data encrypted at rest and in transit.
Open Source Plans
We are preparing to open-source MGML
We plan to release the core MGML library, documentation, and example implementations. This will enable developers and researchers to build on our methods and apply MGML to new domains.
Coming Soon