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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
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
Our
Mission
Build
the
world’s
largest
open-source
Logic
data
set,
called
Aristotle.
Revolutionizing Logical Reasoning
Introducing LogicNet, the pioneering framework that’s redefining the boundaries of logical reasoning in AI models.
Smarter Logic Models
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
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
LogicNet
Roadmap
Advancing
Efficient
Logical
Reasoning
in
AI
Foundation 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 accessInregrated with the Best Tools
Inregrated
with
the
Best
Tools
Seamlessly
connect
with
native
integrations
for
X,
Telegram,
Discord,
Github,...
