Update README.md

README.md

@@ -30,4 +30,6 @@ Lamarck 14B v0.7:  A generalist merge focused on multi-step reasoning, prose, an
 
 Lamarck's performance comes from an ancestry that goes back through careful merges to select finetuning work, upcycled and combined.  Through intermediate merges, [arcee-ai/Virtuoso-Small](https://huggingface.co/arcee-ai/Virtuoso-Small) [sthenno-com/miscii-14b-1225](https://huggingface.co/sthenno-com/miscii-14b-1225) and [VAGOsolutions/SauerkrautLM-v2-14b-DPO](https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO) are emphasized in early layers for extra BBH; later layers add synergistic influence from [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B), [Krystalan/DRT-o1-14B](https://huggingface.co/Krystalan/DRT-o1-14B), [EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2), and [CultriX/Qwen2.5-14B-Wernicke](https://huggingface.co/CultriX/Qwen2.5-14B-Wernicke).
 
+More subjectively, its prose and translation abilities are boosted by repeated re-emphasis of [Krystalan/DRT-o1-14B](https://huggingface.co/Krystalan/DRT-o1-14B) and [underwoods/medius-erebus-magnum-14b](https://huggingface.co/underwoods/medius-erebus-magnum-14b).  Other models found in [sometimesanotion/Qwenvergence-14B-v3-Prose](https://huggingface/sometimesanotion/Qwenvergence-14B-v3-Prose) have their impact on prose quality - and surprising synergy of reasoning.
+
 Kudoes to @arcee-ai, @deepseek-ai, @Krystalan, @underwoods, @VAGOSolutions, @CultriX, @sthenno-com, and @rombodawg whose models had the most influence.  [Vimarckoso v3](https://huggingface.co/sometimesanotion/Qwen2.5-14B-Vimarckoso-v3) has the model card which documents its extended lineage.