SHADA

🧠 Research Focus:

SHADA (Self-supervised Hierarchical Adaptive Deep Algorithm) is a research-grade, multi-phase deep learning training framework unifying SSL, hierarchical hybrid architecture, multi-task optimization, and RL for NLP and CV.

🏗️ Architecture:

• 4-stage hierarchical encoder (ConvNeXt CNN stem → Transformer stages S1–S4) with FPN neck

• Decoder-only LLM for text; DETR-style detection head

• Multi-Head Attention with RoPE, FlashAttention-2 integration, Grouped-Query Attention (GQA), KV-cache support

📈 Training Pipeline:

1. SSL Pre-training (MAE + Contrastive + DINO)

2. Multi-task Intermediate Fine-tuning (GradNorm + curriculum learning)

3. Supervised Fine-tuning (LLRD, R-Drop, GMP pruning)

4. Deployment Optimization (QAT/PTQ quantization, GQA)

🎯 Key Features:

• LoRA (rank-16) injected into all attention projection matrices; <1% of parameters trained

• PPO (RLHF with GAE, KL control) and DPO (direct preference optimization)

• Gradual Magnitude Pruning (GMP), AWQ INT4 / INT8 quantization, QAT

💻 Technologies:

PyTorch 2.2+, FSDP + DeepSpeed ZeRO-3, FlashAttention-2, AutoAWQ, bitsandbytes, LoRA, RoPE