Update README.md

README.md

@@ -8,13 +8,13 @@ metrics:
 - accuracy
 library_name: transformers
 pipeline_tag: feature-extraction
-doi: 10.5281/zenodo.18685168
+doi: 10.5281/zenodo.18727667
 
 ---
 
 # 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.
+**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**
 
@@ -24,7 +24,7 @@ doi: 10.5281/zenodo.18685168
 
 • Scalability: Real-time stellar analysis for future space surveys.
 
-* **Research Link:** https://zenodo.org/records/18685168
+ **Research Link:** https://zenodo.org/records/18727667
 
 ### 1. Technical Feature Map
 The model is designed to process 13 fundamental astronomical parameters:
@@ -42,7 +42,7 @@ The model is designed to process 13 fundamental astronomical parameters:
 
 ### 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 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](https://huggingface.co/datasets/samfatnassi/gaia-dr3)      
 
@@ -57,7 +57,7 @@ from transformers import AutoModel
 dataset = load_dataset("samfatnassi/gaia-dr3", split="train", streaming=True)
 
 # 2. Load the Model
-model = AutoModel.from_pretrained("KilmaAI/SADIM-V2")
+model = AutoModel.from_pretrained("KilmaAI/SADIM-77M")
 
 # Fetch a sample star
 sample_star = next(iter(dataset))