Update README.md

README.md

@@ -14,24 +14,16 @@ library_name: transformers
 
 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
+   - Input: Weight matrix for each model layer
    - 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:
+   - Filter values above numerical threshold (>1e-12) 
    - 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:
+   - Compute Shannon entropy: H = -Σ(pᵢ * log₂(pᵢ)) # measures information content 
+   - Calculate maximum possible entropy: H_max = log₂(n)
    - 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
+   - Results in value between 0 and 1
+     # 0 = single dimension dominance, 1 = uniform dimensional utilization
 
 ## Creating Composite Model
 
@@ -63,6 +55,8 @@ fine_tuned_models: # uncomment the models you want to merge
 
 #- "Qwen/Qwen2.5-7B-Instruct"
 
+#- "EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1"
+
 #- "FourOhFour/Vapor_v2_7B"
 
 #- "Goekdeniz-Guelmez/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2"
@@ -77,21 +71,41 @@ fine_tuned_models: # uncomment the models you want to merge
 
 #- "Orion-zhen/Meissa-Qwen2.5-7B-Instruct"
 
+#- "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v0.9"
+
 #- "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0"
 
+#- "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.1"
+
+#- "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.2"
+
+#- "AmberYifan/Qwen2.5-7B-dpo-2k"
+
+#- "sethuiyer/Qwen2.5-7B-Anvita"
+
 #- "rombodawg/Rombos-LLM-V2.5-Qwen-7b"
 
 #- "Cran-May/T.E-8.1"
 
-#- "thomas-yanxin/XinYuan-Qwen2.5-7B-0917"
-
 #- "beomi/Qwen2.5-7B-Instruct-kowiki-qa"
 
 #- "Orion-zhen/Qwen2.5-7B-Gutenberg-KTO"
 
-#- 'fblgit/cybertron-v4-qw7B-MGS'
+#- "fblgit/cybertron-v4-qw7B-MGS"
+
+#- "nguyentd/FinancialAdvice-Qwen2.5-7B"
 
-#- 'nguyentd/FinancialAdvice-Qwen2.5-7B'
+#- "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B"
+
+#- "edgerunner-ai/EdgeRunner-Command-Nested"
+
+#- "katanemo/Arch-Function-7B"
+
+#- "DeepGlint-AI/llava-mlcd-qwen2.5-7b"
+
+#- "mergekit-community/mergekit-slerp-aflqaqy"
+
+#- "mergekit-community/mergekit-ties-inxwsfo"
 
 #- "Qwen/Qwen2.5-Coder-7B-Instruct"
 
@@ -101,11 +115,13 @@ fine_tuned_models: # uncomment the models you want to merge
 
 #- "Qwen/Qwen2.5-Math-7B"
 
-#- "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B"
+#- "thomas-yanxin/XinYuan-Qwen2.5-7B-0917"
 
-#- "edgerunner-ai/EdgeRunner-Command-Nested"
+#- "jbjeong91/Qwen2.5_7B_IST_StoryGen_vanilla"
 
-#- "katanemo/Arch-Function-7B"
+#- "AmberYifan/Qwen2.5-7B-dpo-2k-hhrlhf"
+
+#- "jbjeong91/Qwen2.5_7B_IST_StoryGen_test2" 
 
 models_dir: "./input_models/"