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@@ -50,40 +50,35 @@ logic, imagination, and aversion to censorship such as: railroading/gaslighting
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  ## Our implementation of Spherical Linear Interpolation for LLM merging: https://github.com/Digitous/LLM-SLERP-Merge
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- ## Note: Skip to the TL;DR section for the finalized design this model is comprised of.
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- ## Language Models and LoRAs Used Credits:
 
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- All models and adapters used are LLaMAv2-13B.
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- # Models:
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- Nous-Hermes
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- Chronos
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- Platypus
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- Airoboros
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- Holodeck
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- # Adapters:
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- Kimiko
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- Janine
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- LIMA RP
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- Also thanks to Meta for LLaMAv2 and deciding to allow the research community at large to benefit from their incredible work.
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- Each model and LoRA was hand picked and considered for what it could contribute to this ensemble.
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  Thanks to each and every one of you for your incredible work developing some of the best things
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  to come out of this community.
 
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  ## Our implementation of Spherical Linear Interpolation for LLM merging: https://github.com/Digitous/LLM-SLERP-Merge
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+ ## What Makes Naberius Special?
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+ By combining zephyr-7b-sft-beta + OpenHermes-2-Mistral-7B and then dolphin-2.2.1-mistral-7b to the result using a minimally destructive merge technique, preserves a large amount of behavior of all three models in a cohesive fashion.
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+ Naberius can: Do coherent roleplay far and beyond any 7B parameter model ever before, as well as follow instructions exceptionally well, especially for a 7B model and as a bonus for being lightweight, incredible inference speed.
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+ Naberius has shown some signs of spacial awareness and does adapt to nuance in conversation. All around a pliable, imaginative, and logic oriented 7B that punches upwards to what feels like a 30B or more at times.
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+ Naberius can't: walk your dog, do your homework, clean your dishes, tell you to turn off the computer and go to bed on time.
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+ # Ensemble Credits:
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+ All models merged to create are LLaMAv2-7B Mistral-7B Series.
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+ zephyr-7b-sft-beta
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+ OpenHermes-2-Mistral-7B
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+ dolphin-2.2.1-mistral-7b
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+ Thanks to Mistral AI for the amazing Mistral LM - and also thanks to Meta for LLaMAv2.
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  Thanks to each and every one of you for your incredible work developing some of the best things
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  to come out of this community.