Update README.md

README.md

@@ -12,47 +12,41 @@ library_name: transformers
 
 ## Model Description
 
-Model created 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 dimensional utilization
+Model created by analyzing and selecting the optimal layers from other Qwen2.5-7B models based on their dimensional utilization efficiency, measured by the Normalized Effective Rank (NER). Computed like:
+
+Singular Value Decomposition:
+   - Input: Weight matrix A ∈ R^(m×n) # m = number of output features, n = number of input features
+   - Compute singular values σᵢ where σᵢ ≥ 0 # σᵢ represents the importance of each dimension
+   - Filter values above numerical threshold (>1e-12) # removes numerical noise from computation
+
+Distribution Normalization:
+   - Sum all singular values: S = Σσᵢ # S acts as normalization factor
+   - Create probability distribution: pᵢ = σᵢ/S # converts singular values to probabilities summing to 1
+
+Entropy Calculation:
+   - Compute Shannon entropy: H = -Σ(pᵢ * log₂(pᵢ)) # measures information content of distribution
+   - Calculate maximum possible entropy: H_max = log₂(n) # n = number of singular values
+   where n is the number of singular values # maximum entropy occurs when all dimensions contribute equally
+
+Normalization:
+   - Final NER score = H/H_max # normalizes score to [0,1] range
+   - Results in value between 0 and 1 # 0 = single dimension dominance, 1 = perfect dimensional utilization
+   - Higher scores indicate more uniform dimensional utilization
+
+## Creating Composite Model
+
+Layer Analysis:
+   - Download base and fine-tuned models from Hugging Face Hub # fetches models using Hugging Face API
+   - Calculate Normalized Effective Rank (NER) for each layer within each model # process each independently
+
+Layer Selection:
+   - Identify common layer structures across models 
+   - Define model and layer name pairs that have highest NER for each layer based on their NER scores 
+
+Model Composition:
+   - Incrementally build a composite model using layer with highest NER from model pool. 
+
+Output Generation:
+   - Save merge reports documenting layer sources 
+   - Copy config and tokenizer files from base model
+   - Save the composite model with complete weights # model ready to use