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license: cc-by-nc-2.0 |
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by David, Fernando and Eric |
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Sponsored by: [VAGO Solutions](https://vago-solutions.de) |
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[](https://discord.gg/cognitivecomputations) |
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Discord: https://discord.gg/cognitivecomputations |
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An experimentation regarding 'lasering' each expert to denoise and enhance model capabilities. |
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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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# Laserxtral - 4x7b (all, except for base, lasered using laserRMT) |
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This model is a Mixture of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models: |
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* [cognitivecomputations/dolphin-2.6-mistral-7b-dpo](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo) |
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* [mlabonne/Marcoro14-7B-slerp (base)](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) |
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* [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B) |
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* [Q-bert/MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling) |
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* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/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 observed very high truthfulness and high reasoning capabilities. |
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# Evals |
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