Grafi AI Data Layer
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The Data Layer is one of the core components of the Grafilab ecosystem, designed to aggregate, validate, and label. This AI Data Layer is crucial for ensuring privacy, security, and accessibility in the management of computational tasks and AI stats within the Grafi ecosystem.
Key Components of the Data Layer
1. Grafi AI Node:
Coming Soon
2. Data Aggregation:
- The Data Aggregation processes the raw data received from contributors. It organizes and labelling relevant data to data swarm, allowing for secure and immutable data records.
- Our data privacy layer encrypted all data that inflow to our data layer.
How the Data Layer Works
- Data Collection:
- Data is collected from various sources, including CeDePIN Cloud, Inference API, AI Apps/Agents that joined the Grafi ecosystem. This raw data could represent subscription execution details, performance metrics, and user usage statistics.
- Data is collected from various sources, including CeDePIN Cloud, Inference API, AI Apps/Agents that joined the Grafi ecosystem. This raw data could represent subscription execution details, performance metrics, and user usage statistics.
- Aggregation and Validation:
- The data is passed through the Grafi AI Data Layer, which act as data aggregators. These layer validate the accuracy and completeness of the data before it is passed on for further processing. Validators ensure the integrity of the data quality, minimizing errors, analyze trends and tracking user behaviour.
- The data is passed through the Grafi AI Data Layer, which act as data aggregators. These layer validate the accuracy and completeness of the data before it is passed on for further processing. Validators ensure the integrity of the data quality, minimizing errors, analyze trends and tracking user behaviour.
- Data Labeling:
- Once validated, the data pass to our Grafi Data layer, it will be labelled and assigned into data swarm for modeling fine tuning.
- Once validated, the data pass to our Grafi Data layer, it will be labelled and assigned into data swarm for modeling fine tuning.
- Model Fine Tuning:
- High quality data in data swarm will be used to fine tuning Grafilab customised MoE LLM and other smaller LLM to serve different needs.