Update README.md

README.md

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 ## Model Description
 
-Composite model created using normalized effective rank.
+Model merging by analyzing and selecting optimal layers based on dimensional utilization efficiency. The process follows these steps:
+
+Layer Analysis
+- Downloads base and fine-tuned models from Hugging Face Hub
+- Calculates Normalized Effective Rank (NER) for each layer
+- NER measures how effectively each layer utilizes its dimensions through entropy analysis of singular value distributions
+
+Layer Selection
+- Identifies common layer structures across models
+- Ranks layers based on their NER scores
+- Selects highest-performing layers from each model
+- Creates a mapping of optimal layer sources
+
+Model Composition
+- Creates a new model starting from the base architecture
+- Systematically replaces layers with their highest-performing counterparts
+- Preserves model architecture while optimizing layer-wise performance
+- Maintains compatibility with original tokenizer and configuration
+
+Output Generation
+- Saves the composite model with complete weights and configuration
+- Generates detailed merge reports documenting layer sources
+- Copies necessary tokenizer files from base model
+
+NER measures how effectively a neural network layer utilizes its available dimensions through entropy analysis of its singular value distribution. The calculation proceeds as follows:
+
+1. **Singular Value Decomposition**
+   - Input: Weight matrix A ∈ R^(m×n)
+   - Compute singular values σᵢ where σᵢ ≥ 0
+   - Filter values above numerical threshold (>1e-12)
+
+2. **Distribution Normalization**
+   - Sum all singular values: S = Σσᵢ
+   - Create probability distribution: pᵢ = σᵢ/S
+   
+3. **Entropy Calculation**
+   - Compute Shannon entropy: H = -Σ(pᵢ * log₂(pᵢ))
+   - Calculate maximum possible entropy: H_max = log₂(n)
+   where n is the number of singular values
+
+4. **Normalization**
+   - Final NER score = H/H_max
+   - Results in value between 0 and 1
+   - Higher scores indicate more uniform dimen