SHADA
Self-supervised · Hierarchical · Adaptive · Deep · Algorithm
A unified deep learning training framework synthesizing self-supervised learning and hierarchical architecture into a single modular pipeline for high-performance AI.
What is SHADA?
SHADA is a research-grade training framework designed to combine the most effective modern AI training paradigms into one cohesive, composable system. Rather than treating self-supervised pre-training, fine-tuning, and deployment optimization as separate workflows, SHADA integrates them into a structured four-phase pipeline.
The framework is modality-agnostic — its hierarchical encoder processes both image and text inputs — and is designed to scale from edge devices to large-scale multi-domain training clusters.
SHADA is currently in research phase. Performance targets are based on initial simulations and design constraints.
Six Design Pillars
| Pillar |
|---|
| Self-Supervised Pre-training |
| Hierarchical Learning |
| Adaptive Optimization |
| Hybrid Architecture |
| Multi-Task Training |
| Efficiency-First Design |
Why SHADA?
The Problem
- Large quantities of labeled data required for each new task
- Separate, sequential pipelines for pre-training, fine-tuning, and deployment
- Task-specific architectures that don't transfer across modalities
- Post-hoc engineering for efficiency after training is complete
- No unified framework for NLP and Computer Vision training
SHADA's Solution
- MAE + DINO + SimCLR — Joint objectives — 10–100× less labeled data
- Unified 4-phase pipeline — One coherent, composable system end-to-end
- Modality-agnostic encoder — Handles both images and text natively
- Efficiency-first design — QAT, GMP, GQA baked in from the start
- Cross-modal training — Single framework for NLP + Computer Vision tasks
Traditional vs. SHADA Approach
The Technical Core
∑Master Loss Function
Coordinates all objectives simultaneously across training phases.
Four-Phase Training Pipeline
Self-Supervised Pre-training
~150K stepsThree SSL objectives combined into a joint pre-training signal over unlabeled data.
Randomly masks 75% of patches. Trains encoder-decoder to reconstruct original signal. Decoder discarded after pre-training.
SimCLR-style contrastive. Pulls positive pairs together, pushes negatives apart. τ = 0.07.
EMA teacher-student distillation. Momentum = 0.996. No contrastive negatives required.
Multi-Task Intermediate Fine-tuning
~50K stepsDynamic task balancing with curriculum learning across labeled and unlabeled data.
Dynamically adjusts per-task loss weights. Computes gradient norms, defines target norms per training speed. α = 1.5.
EMA per-sample difficulty scores. Temperature annealed T = 5.0 → 1.0. Easy ratio ramps p=0.2 → p=0.8 over 30K steps.
Supervised Fine-tuning
10–30K stepsLLRD prevents catastrophic forgetting. SWA finds flatter minima. GMP applies polynomial sparsity with unstructured and head-level modes.
Deployment Optimization
~5K steps + postOptional RL Alignment
OptionalActor + Critic + Reward + Reference model. GAE γ=0.99 λ=0.95. KL penalty: adj_reward = r − β·KL. ε-clip = 0.2, 4 PPO epochs per batch.
Direct from (chosen, rejected) pairs. No reward model needed. β = 0.1. More stable than PPO for language alignment.
Results & Targets
Design-Target Benchmarks
| Task | Baseline | SHADA Target |
|---|---|---|
| IMDB Sentiment (NLP) | 87.3% | 91–94% |
| AG News Classification | 91.1% | 94–96% |
| ImageNet-1K (CV) | 76.4% | 82–85% |
| Multi-label NER | 79.5% | 84–87% |
| Text + Image MTL | — | 80–83% |
Phase 1 Loss Curve (Design)
SSL joint loss convergence over ~150K pre-training steps
Efficiency Gains Summary
How to Use SHADA
Install the SHADA Python library and integrate it directly into your machine learning pipelines. The API follows the sklearn pattern with fit/predict methods for seamless adoption.
# From PyPI (recommended) pip install shadax # From GitHub pip install git+https://github.com/OmarAlghafri/SHADA-API-Core-Reference.git # From local wheel pip install dist/*.whl
API Reference
SHADA(...)Initialize model with tier and parameters
model.fit(X, y)Train the model on data
model.predict(X)Predict class labels
model.predict_proba(X)Get class probabilities
model.score(X, y)Calculate accuracy
model.extract_features(X)Extract learned features
model.save(path)Save model to file
model.load(path)Load model from file
Input Formats
numpy.ndarraynumpy.ndarraytorch.TensorWhere SHADA Applies
Natural Language Processing
Fine-grained emotion classification from long-form reviews and social text using the IMDB-style pipeline.
Multi-class news, topic, and intent classification (AG News, DBpedia) via Phase 3 supervised fine-tuning.
Multi-label token classification with hierarchical attention layers and label-smoothed cross-entropy.
Span extraction over long documents using the transformer stages of the SHADA encoder.
Computer Vision
Hierarchical ConvNeXt stem plus transformer stages for powerful multi-scale visual representations.
Pre-trained SHADA encoder as a frozen or fine-tuned feature backbone for downstream CV tasks.
MAE masking + DINO distillation produces linear-probe-ready features without any labeled data.
Feature Pyramid Network fusion from all four SHADA stages for object detection tasks.
Multi-modal Learning
Pre-train on unlabeled text, adapt to vision tasks with minimal labeled data using shared representations.
Simultaneous training on NLP and CV tasks with GradNorm preventing modality imbalance.
Evaluate one model across tasks from different domains, enabled by the modality-agnostic encoder.
Initialize from CLIP, LLaMA, ViT, or DINOv2 checkpoints and continue with SHADA phases.
Quick Reference
# Clone the repository git clone https://github.com/OmarAlghafri/SHADA-API-Core-Reference.git cd shada # Create virtual environment python -m venv .venv source .venv/bin/activate # Windows: .venv\Scripts\activate # Install dependencies pip install -r requirements.txt # Or install as package pip install -e .
API Core Reference
SHADATrainerMain training orchestrator. Manages all 4 phases and configuration.
SHADAConfigConfiguration dataclass for model tier, phases, modality, and hyperparameters.
SHADAEvaluatorPost-training evaluation with metrics, confusion matrix, and comparison tools.
SHADAExporterModel export to ONNX, TorchScript, and HuggingFace Hub formats.
create_model(tier)Factory function returning a SHADA model for the given tier string.
SHADADataModuleDataset loading, preprocessing, augmentation, and dataloader management.
Research Resources
Omar Alghafri
The mind behind SHADA — bridge the gap between raw data and intelligent hierarchical structures. Developing next-generation optimization strategies for complex multi-modal systems.