refine Model Storage Optimisation naming (#602)

make the module naming more clear

README.md

@@ -19,7 +19,7 @@ This repository contains a curated list of awesome open source libraries that wi
 | [📈 Evaluation & Observability](#evaluation-and-observability) | [🔍 Explainability & Interpretability](#explainability-and-interpretability) | [🎁 Feature Store](#feature-store) |
 | [🔴 Industry-strength Anomaly Detection](#industry-strength-ad) | [👁️ Industry-strength Computer Vision](#industry-strength-cv) | [🔠 Industry-strength Natural Language Processing](#industry-strength-nlp) |
 | [🙌 Industry-strength Recommender System](#industry-strength-recsys) | [🍕 Industry-strength Reinforcement Learning](#industry-strength-rl) | [📊 Industry-strength Visualisation](#industry-strength-visualisation) |
-| [📅 Metadata Management](#metadata-management) | [📜 Model, Data & Experiment Tracking](#model-data-and-experiment-tracking) | [🔩 Model Compilation, Compression & Optimization](#model-compilation-compression-and-optimization) |
+| [📅 Metadata Management](#metadata-management) | [📜 Model, Data & Experiment Tracking](#model-data-and-experiment-tracking) | [🔩 Model Storage Optimisation](#model-storage-optimisation) |
 | [🔥 Neural Search](#neural-search) | [🧮 Optimized Computation](#optimized-computation) | [🔏 Privacy & Security](#privacy-security) |
 | [🏁 Training Orchestration](#training-orchestration) |
 
@@ -593,7 +593,7 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 * [Weights & Biases](https://github.com/wandb/wandb) ![](https://img.shields.io/github/stars/wandb/wandb.svg?style=social) - Weights & Biase is a machine learning experiment tracking, dataset versioning, hyperparameter search, visualization, and collaboration.
 
 
-## Model Compilation, Compression and Optimization
+## Model Storage Optimisation
 * [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) ![](https://img.shields.io/github/stars/casper-hansen/AutoAWQ.svg?style=social) - AutoAWQ is an easy-to-use package for 4-bit quantized models.
 * [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) ![](https://img.shields.io/github/stars/AutoGPTQ/AutoGPTQ.svg?style=social) - An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
 * [AWQ](https://github.com/mit-han-lab/llm-awq) ![](https://img.shields.io/github/stars/mit-han-lab/llm-awq.svg?style=social) - Activation-aware Weight Quantization for LLM Compression and Acceleration.