Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- samfatnassi/gaia-dr3
+language:
+- en
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: feature-extraction
+doi: 10.5281/zenodo.18685168
+
+---
+
+# SADIM: Stellar Intelligence Framework
+
+**SADIM-V2** is a specialized AI model trained on the **Gaia Data Release 3 (DR3)** catalog. It bridges the gap between massive raw astronomical observations and actionable scientific insights.
+
+**Research Objectives**
+
+• Galactic Archaeology: Identifying stellar streams and ancient structures.
+
+• Big Data Optimization: Providing an AI-ready interface for 1B+ records.
+
+• Scalability: Real-time stellar analysis for future space surveys.
+
+* **Research Link:** https://zenodo.org/records/18685168
+
+### 1. Technical Feature Map
+The model is designed to process 13 fundamental astronomical parameters:
+
+| Feature Name | Description |
+| :--- | :--- |
+| **source_id** | Unique Gaia DR3 Identifier |
+| **ra / dec** | Celestial Equatorial Coordinates |
+| **l / b** | Galactic Coordinates (Disk Alignment) |
+| **pmra / pmdec** | Kinematics (Proper Motion Velocity) |
+| **d_pc** | Distance in Parsecs (1/parallax) |
+| **x, y, z** | 3D Heliocentric Cartesian Mapping |
+| **abs_m** | Absolute Magnitude (Intrinsic Brightness) |
+| **bp_rp** | Color Index (Temperature Indicator) |
+
+### 2. When to use the Model vs. the Dataset?
+
+* **Use the SADIM-V2 77M Model:** For fast inference, predicting missing stellar properties, or automating the classification of new astronomical data.
+* **Use the Gaia-DR3 Dataset:** For deep-dive research, historical record querying, or training your own custom neural networks.
+* **Dataset Link:** [samfatnassi/gaia-dr3](https://huggingface.co/datasets/samfatnassi/gaia-dr3)      
+
+### 3. Quick Start (Python)
+Since the dataset contains over **1 Billion records**, we recommend using **Streaming Mode**:
+
+```python
+from datasets import load_dataset
+from transformers import AutoModel
+
+# 1. Access the Data
+dataset = load_dataset("samfatnassi/gaia-dr3", split="train", streaming=True)
+
+# 2. Load the Model
+model = AutoModel.from_pretrained("KilmaAI/SADIM-V2")
+
+# Fetch a sample star
+sample_star = next(iter(dataset))
+print(f"Analyzing Star ID: {sample_star['source_id']}")
+
+