Update README.md

README.md

@@ -23,9 +23,11 @@ pipeline_tag: text-generation
 
 **Update:**  Lamarck has beaten its predecessor [Qwen2.5-14B-Vimarckoso-v3](https://huggingface.co/sometimesanotion/Qwen2.5-14B-Vimarckoso-v3) and, for the moment, taken the #1 average score for 14 billion parameter models.  Counting all the way up to 32 billion parameters, it's #7.  Humor me, I'm giving our guy his meme shades!
 
-Lamarck 14B v0.6:  A generalist merge focused on multi-step reasoning, prose, multi-language ability, and code.  It is based on components that have punched above their weight in the 14 billion parameter class.
+Lamarck 14B v0.6:  A generalist merge focused on multi-step reasoning, prose, multi-language ability, and code.  It is based on components that have punched above their weight in the 14 billion parameter class.  Here you can see a comparison between Lamarck and other top-performing merges and finetunes:
 
-The tempo of Lamarck releases slowed because improving IFEVAL while maintaining other scores is no small task.  Previous releases were based on a SLERP merge of model_stock->della branches focused on reasoning and prose.  The prose branch got surprisingly good at reasoning, and the reasoning branch became a strong generalist in its own right.  Some of you have already downloaded it as [sometimesanotion/Qwen2.5-14B-Vimarckoso-v3](https://huggingface.co/sometimesanotion/Qwen2.5-14B-Vimarckoso-v3).
+![Lamarck.webp](https://huggingface.co/sometimesanotion/Lamarck-14B-v0.6/resolve/main/comparison.png)
+
+Previous releases were based on a SLERP merge of model_stock->della branches focused on reasoning and prose.  The prose branch got surprisingly good at reasoning, and the reasoning branch became a strong generalist in its own right.  Some of you have already downloaded it as [sometimesanotion/Qwen2.5-14B-Vimarckoso-v3](https://huggingface.co/sometimesanotion/Qwen2.5-14B-Vimarckoso-v3).
 
 Lamarck 0.6 aims to build upon Vimarckoso v3's all-around strengths by using breadcrumbs and DELLA merges, with highly targeted weight/density gradients for every four layers and special andling for the first and final two layers.  This approach selectively merges the strongest aspects of its ancestors.