Update README.md

README.md

@@ -45,21 +45,25 @@ It benefits from use of evolutionary merges, particularly from [CultriX/SeQwence
 
 - **[arcee-ai/Virtuoso-Small](https://huggingface.co/arcee-ai/Virtuoso-Small)** - A brand new model from Arcee, refined from the notable cross-architecture Llama-to-Qwen distillation [arcee-ai/SuperNova-Medius](https://huggingface.co/arcee-ai/SuperNova-Medius).  The first two layers are nearly exclusively from Virtuoso.  It has proven to be a well-rounded performer, and contributes a noticeable boost to the model's prose quality.
 
-- **[CultriX/SeQwence-14B-EvolMerge](http://huggingface.co/CultriX/SeQwence-14B-EvolMerge)** - A well-rounded model, with interesting gains for instruction following while remaining strong for reasoning.
+- **[CultriX/SeQwence-14B-EvolMerge](http://huggingface.co/CultriX/SeQwence-14B-EvolMerge)** - A top contender on reasoning benchmarks.  
+
+**Reason:** While Virtuoso is the strongest influence the starting ending layers, the reasoning mo
 
 - **[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.
 
+- **[VAGOsolutions/SauerkrautLM-v2-14b-DPO](https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO)** - This model's influence is understated, but aids BBH and coding capability.
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 **Prose:** While the prose module is gently applied, its impact is noticeable on Lamarck 0.3's prose quality, and a DELLA merge re-emphasizes the contributions of two models particularly:
 
-- **[sthenno-com/miscii-14b-1028](https://huggingface.co/sthenno-com/miscii-14b-1028)** - 
+- **[sthenno-com/miscii-14b-1028](https://huggingface.co/sthenno-com/miscii-14b-1028)**
 
-- **[underwoods/medius-erebus-magnum-14b](https://huggingface.co/underwoods/medius-erebus-magnum-14b)** - 
+- **[underwoods/medius-erebus-magnum-14b](https://huggingface.co/underwoods/medius-erebus-magnum-14b)**
 
 **Model stock:** Two model_stock merges, specialized for specific aspects of performance, are used to mildly influence a large range of the model.
 
-- **[sometimesanotion/lamarck-14b-reason-model_stock](https://huggingface.co/sometimesanotion/lamarck-14b-reason-model_stock)** - This means [VAGOsolutions/SauerkrautLM-v2-14b-DPO](https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO) has a contribution, most likely noticeable in BBH
+- **[sometimesanotion/lamarck-14b-reason-model_stock](https://huggingface.co/sometimesanotion/lamarck-14b-reason-model_stock)** 
 
-- **[sometimesanotion/lamarck-14b-prose-model_stock](https://huggingface.co/sometimesanotion/lamarck-14b-prose-model_stock)** - This brings in a little influence from [EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2), [oxyapi/oxy-1-small](https://huggingface.co/oxyapi/oxy-1-small), [allura-org/TQ2.5-14B-Sugarquill-v1](https://huggingface.co/allura-org/TQ2.5-14B-Sugarquill-v1).
+- **[sometimesanotion/lamarck-14b-prose-model_stock](https://huggingface.co/sometimesanotion/lamarck-14b-prose-model_stock)** - This brings in a little influence from [EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2), [oxyapi/oxy-1-small](https://huggingface.co/oxyapi/oxy-1-small), and [allura-org/TQ2.5-14B-Sugarquill-v1](https://huggingface.co/allura-org/TQ2.5-14B-Sugarquill-v1).
 
 **Note on abliteration:**  This author believes that adjacent services and not language models themselves are where guardrails are best placed.  Effort to de-censor Lamarck will resume after the model has been further studied.