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--- |
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library_name: transformers |
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tags: |
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- mergekit |
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- merge |
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license: apache-2.0 |
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base_model: |
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- arcee-ai/Virtuoso-Small |
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- CultriX/SeQwence-14B-EvolMerge |
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- CultriX/Qwen2.5-14B-Wernicke |
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- sometimesanotion/lamarck-14b-prose-model_stock |
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- sometimesanotion/lamarck-14b-if-model_stock |
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- sometimesanotion/lamarck-14b-reason-model_stock |
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language: |
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- en |
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--- |
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![Lamarck.webp](https://huggingface.co/sometimesanotion/Lamarck-14B-v0.3-experimental/resolve/main/Lamarck.webp) |
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--- |
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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. |
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### Merge Strategy: |
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- Two model_stocks used to begin specialized branches for reasoning and prose quality. |
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- For refinement on Virtuoso as a base model, DELLA and SLERP include the model_stocks while re-emphasizing selected ancestors. |
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- For integration, a SLERP merge of Virtuoso with the converged branches. |
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- For finalization, a TIES merge. |
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### Ancestor Models: |
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**Top influences:** These ancestors are base models and present in the model_stocks, but are heavily re-emphasized in the DELLA and SLERP merges. |
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- **[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. |
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- **[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. |
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- **[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. |
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![graph.png](https://huggingface.co/sometimesanotion/Lamarck-14B-v0.3-experimental/resolve/main/graph.png) |
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