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Transition...
Think BIGGER,
with smaller models
The future of AI is Cooperative: Community generated data harnessed by compact AI models to produce results.Precision Meets Efficiency: 7B Models Outperforming 70B Giants
We’re fine-tuning the open-source LogicNet 7B parameter model to not just match, but surpass the logical reasoning capabilities of 70B parameter models.
Our Mission
LogicNet-7b open source model to top the ZebraLogic Benchmark.Build the world’s largest open-source Logic data set, called Aristotle.
Versatility: Reduced model size, lower computational overhead.
Efficiency: Effective in diverse logical reasoning tasks and domains.
Accessibility: Open-source models, accelerating research and innovation.
Our Approach
These approaches synergize to create a powerfully efficient model, excelling in logical reasoning across various fields while maintaining a compact 7B parameter size.Synthetic Dataset Fine-Tuning
Miners generate tailored synthetic datasetsTargets specific improvements in logical reasoningAddresses data scarcity and enhances model versatilityMulti-Domain Learning
Incorporates diverse data pipelines for comprehensive reasoning skillsSpans philosophy, coding, medicine, economics, law, and academiaBroadens model applicability and deepens analytical capabilitiesLogicNet Roadmap
Advancing Efficient Logical Reasoning in AIFoundation and Benchmarking
Establish comprehensive logical reasoning benchmark suite using Zebra BenchDeploy baseline open source 7B parameter model with LogicNet framework integrationImplement synthetic data generation pipeline with validator quality controlDeploy multi-task learning framework for dataset enhancementCreate evaluation system for model comparison against 70B+ modelsMinimize overfitting exploitationsInnovation in Model Architecture, Data Set Growth and Training
Launch continuous data generation system through a duel-tasked miner networkImplement quality-driven selection mechanisms through validatorsDeploy automated curation pipeline for synthetic datasetDevelop specialized logical reasoning training protocolsCreate robust performance tracking metricsScaling and Research
Scale architecture to optimize 7B parameter performanceImplement advanced overfitting prevention mechanismsDeploy continuous model improvement protocolsLaunch research collaboration platformImplement interpretability analysis toolsDeploy model explainability frameworksSpecialization Platform
Launch front-end ‘Logical Specialization’ interfaceDeploy payment and monetization mechanism for validatorsCreate specialized model tuning protocols and implement custom dataset creation toolsDeploy enterprise-grade API access
