added research capibilities

Documentation/Technical_Report.md

@@ -1,12 +1,23 @@
 # Documentation for Gemma Chat Demo
 
-## Model Research
+## Model Choice and Research
+
+Basing off of the Geema 3 technical report, I will analyze and compare different model sizes to determine the most suitable one for deployment in this project. For this portion, I will take the model's size, performance benchmarks, inference efficiency and memory requirements into consideration. The goal of this analysis is to strike a balance between computational cost and model quality.
 
 ### Model Overview
 
+The Gemma 3 family consists of four model sizes, each with increasing capabilities and resource requirements:
+
+| Model | Parameters | Vision Encoder | Total Size | Context Length | Key Capabilities |
+|-------|------------|---------------|------------|----------------|------------------|
+| Gemma 3-1B | 698M language + 302M embeddings | None | 1B | 32K tokens | Basic text generation; no vision capabilities, extrmely lightweight |
+| Gemma 3-4B | 3.2B language + 675M embeddings + 417M vision | SigLIP | 4.3B | 128K tokens | Multimodal with good balance of performance and efficiency |
+| Gemma 3-12B | 10.8B language + 1B embeddings + 417M vision | SigLIP | 12.2B | 128K tokens | Strong performance across all tasks with reasonable resource needs |
+| Gemma 3-27B | 25.6B language + 1.4B embeddings + 417M vision | SigLIP | 27.4B | 128K tokens | Best performance; comparable to Gemini 1.5 Pro on benchmarks |
+
 ### Performance Analysis
 
-### HuggingSpace Resources
+### HuggingFace Spaces Resources
 
 ## User Research