Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# HLO Feature Dataset for Deep Learning Workloads
+
+[![Dataset](https://img.shields.io/badge/HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/your-username/hlo-feature-dataset)
+
+## Dataset Summary
+The **HLO Feature Dataset** provides High-Level Optimizer (HLO) graph features extracted from various deep learning model training runs. Each sample represents a unique configuration of neural network training, paired with graph-based features suitable for machine learning tasks such as:
+
+- Runtime and resource prediction
+- AI workload optimization in HPC environments
+- Graph neural network (GNN) research on compiler-level representations
+- Scheduling and efficiency analysis for GPU-based training
+
+The dataset includes metadata for each training run and corresponding `.npz` files containing serialized HLO graph features.
+
+---
+
+## Supported Tasks and Benchmarks
+- 🕒 **Runtime Prediction**
+- 📊 **Resource Utilization Estimation**
+- ⚙️ **Graph-Based Neural Architecture Analysis**
+- 🚀 **HPC & GPU Scheduling Optimization**
+
+This dataset can be used to train models that predict execution time, memory usage, or optimize scheduling strategies based on compiler graph features.
+
+---
+
+## Dataset Structure
+
+Each sample consists of:
+- **Metadata**: Model configuration, hardware specs, and performance metrics (from `dataset-new.csv`).
+- **HLO Graph Features**: Stored in `.npz` files containing:
+  - `node_opcode`: Operation codes
+  - `node_feat`: Node feature matrix
+  - `edge_index`: Graph topology
+  - `node_config_ids`: Config identifiers
+  - `node_splits`: Graph partition info
+
+---
+
+## Features
+| Feature            | Type     | Description                          |
+|--------------------|----------|--------------------------------------|
+| name               | string   | Model name                           |
+| optimizer          | string   | Optimizer used                       |
+| batch, epochs      | int      | Training parameters                  |
+| learn_rate         | float    | Learning rate                        |
+| gpu_name           | string   | GPU model                            |
+| fit_time           | float    | Total training time                  |
+| npz_path           | string   | Path to HLO feature `.npz` file      |
+| ...                | ...      | Additional GPU & utilization metrics |
+
+The `.npz` file contains graph data relevant for GNNs or ML models.
+
+---
+
+## Usage Example
+
+```python
+from datasets import load_dataset
+import numpy as np
+
+# Load metadata
+dataset = load_dataset("your-username/hlo-feature-dataset")
+sample = dataset['train'][0]
+
+# Load HLO features
+npz_file = sample['npz_path']
+data = np.load(npz_file)
+
+node_features = data['node_feat']
+edges = data['edge_index']