Update README.md

README.md

@@ -17,17 +17,24 @@ language:
 ![Lamarck.webp](https://huggingface.co/sometimesanotion/Lamarck-14B-v0.3-experimental/resolve/main/Lamarck.webp)
 ---
 
-Lamarck-14B version 0.3 is strongly based on [arcee-ai/Virtuoso-Small](https://huggingface.co/arcee-ai/Virtuoso-Small) as a diffuse influence for prose and reasoning.  Arcee's pioneering use of distillation and innovative merge techniques create a diverse knowledge pool for its models.
+### Overview:
 
-Thanks go to @arcee-ai's team for the bounties of mergekit, and to @CultriX for the helpful examples of memory-efficient sliced merges and evolutionary merging.  Their contribution of tinyevals on version 0.1 of Lamarck did much to validate and focus the build process of this model.
+Lamarck-14B version 0.3 is the product of a custom toolchain built around multi-stage templated merges, with an end-to-end strategy for giving each ancestor model priority where it's most effective.  It is strongly based on [arcee-ai/Virtuoso-Small](https://huggingface.co/arcee-ai/Virtuoso-Small) as a diffuse influence for prose and reasoning.  Arcee's pioneering use of distillation and innovative merge techniques create a diverse knowledge pool for its models.
 
-### Overview:
+** The merge strategy of Lamarck 0.3 can be summarized as:**
 
 - Two model_stocks used to begin specialized branches for reasoning and prose quality.
 - For refinement on Virtuoso as a base model, DELLA and SLERP include the model_stocks while re-emphasizing selected ancestors.
 - For integration, a SLERP merge of Virtuoso with the converged branches.
 - For finalization, a TIES merge.
 
+![graph.png](https://huggingface.co/sometimesanotion/Lamarck-14B-v0.3-experimental/resolve/main/graph.png)
+
+### Thanks go to:
+
+- @arcee-ai's team for the bounties of mergekit and the exceptional Virtuoso Small model
+- @CultriX for the helpful examples of memory-efficient sliced merges and evolutionary merging.  Their contribution of tinyevals on version 0.1 of Lamarck did much to validate the hypotheses of the process used here.
+
 ### Ancestor Models:
 
 **Top influences:** These ancestors are base models and present in the model_stocks, but are heavily re-emphasized in the DELLA and SLERP merges.
@@ -38,9 +45,7 @@ Thanks go to @arcee-ai's team for the bounties of mergekit, and to @CultriX for
 
 - **[CultriX/Qwen2.5-14B-Wernicke](http://huggingface.co/CultriX/Qwen2.5-14B-Wernicke)** - A top performer for Arc and GPQA, Wernicke is re-emphasized in small but highly-ranked portions of the model.
 
-![graph.png](https://huggingface.co/sometimesanotion/Lamarck-14B-v0.3-experimental/resolve/main/graph.png)
-
-### Merge Strategy:
+### Merge YAML:
 
 ```yaml
 name:                lamarck-14b-reason-della                  # This contributes the knowledge and reasoning pool, later to be merged