sometimesanotion
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README.md
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![Lamarck.webp](https://huggingface.co/sometimesanotion/Lamarck-14B-v0.3/resolve/main/Lamarck.webp)
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---
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-
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Lamarck-14B is a carefully designed merge which emphasizes [arcee-ai/Virtuoso-Small](https://huggingface.co/arcee-ai/Virtuoso-Small) in early and finishing layers, and midway features strong influence on reasoning and prose from [CultriX/SeQwence-14B-EvolMerge](http://huggingface.co/CultriX/SeQwence-14B-EvolMerge) especially, but a number of other models as well through its model_stock.
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- @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 DELLA->SLERP gradient process used here.
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- The authors behind the capable models that appear in the model_stock. The boost to prose quality is already noticeable.
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### Models Merged
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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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**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.
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### Configuration
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The following YAML
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```yaml
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name: lamarck-14b-reason-della # This contributes the knowledge and reasoning pool, later to be merged
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![Lamarck.webp](https://huggingface.co/sometimesanotion/Lamarck-14B-v0.3/resolve/main/Lamarck.webp)
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---
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# merge
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Lamarck-14B is a carefully designed merge which emphasizes [arcee-ai/Virtuoso-Small](https://huggingface.co/arcee-ai/Virtuoso-Small) in early and finishing layers, and midway features strong influence on reasoning and prose from [CultriX/SeQwence-14B-EvolMerge](http://huggingface.co/CultriX/SeQwence-14B-EvolMerge) especially, but a number of other models as well through its model_stock.
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- @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 DELLA->SLERP gradient process used here.
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- The authors behind the capable models that appear in the model_stock. The boost to prose quality is already noticeable.
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### Models Merged
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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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**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.
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### Configuration
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The following YAML configuration was used to produce this model:
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```yaml
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name: lamarck-14b-reason-della # This contributes the knowledge and reasoning pool, later to be merged
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