# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("KilmaAI/Sadim-77M", dtype="auto")Quick Links
SADIM: Stellar Intelligence Framework
SADIM-77M 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/18727667
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 77 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
3. Quick Start (Python)
Since the dataset contains over 1 Billion records, we recommend using Streaming Mode:
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-77M")
# Fetch a sample star
sample_star = next(iter(dataset))
print(f"Analyzing Star ID: {sample_star['source_id']}")
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KilmaAI/Sadim-77M")