D^2-MoE: Delta Decompression for MoE-based LLMs Compression

Decentralized personalized federated learning based on a conditional sparse-to-sparser scheme (TNNLS)

[AICCSA 2025] Official Implementation of the paper "Z-Pruner: Post-Training Pruning of Large Language Models for Efficiency without Retraining".

Certain Finetuning , Inferencing, Optimization Tasks(Pruning, Distillation) for better LLM Performance

Developing efficient deep learning models for real-world use. Covers knowledge distillation, quantization, pruning, and more. Focused on reducing size and latency while preserving accuracy. Includes training pipelines, visualizations, and performance reports.