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README.md
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@@ -8,8 +8,17 @@ An experimentation regarding 'lasering' each expert to denoise and enhance model
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This model has half size in comparison to the Mixtral 8x7b Instruct. And it basically has the same level of performance (we are working to get a better MMLU score).
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It follows the implementation of laserRMT @ https://github.com/cognitivecomputations/laserRMT
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Here, we are controlling layers checking which ones have lower signal to noise ratios (which are more subject to noise), to apply Laser interventions, still using Machenko Pastur to calculate this ratio.
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We intend to be the first of a family of experimentations being carried out @ Cognitive Computations.
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This model has half size in comparison to the Mixtral 8x7b Instruct. And it basically has the same level of performance (we are working to get a better MMLU score).
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Used models (all lasered using laserRMT):
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cognitivecomputations/dolphin-2.6-mistral-7b-dpo
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mlabonne/Marcoro14-7B-slerp
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beowolx/CodeNinja-1.0-OpenChat-7B
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Q-bert/MetaMath-Cybertron-Starling
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WizardLM/WizardMath-7B-V1.1
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It follows the implementation of laserRMT @ https://github.com/cognitivecomputations/laserRMT
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Here, we are controlling layers checking which ones have lower signal to noise ratios (which are more subject to noise), to apply Laser interventions, still using Machenko Pastur to calculate this ratio.
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We intend to be the first of a family of experimentations being carried out @ Cognitive Computations.
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In this experiment we have observe very high truthfulness and high reasoning capabilities.
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