Data Layer
Last updated
Last updated
The NeurochainAI Data Layer is a key part of the L1 Decentralized AI (DeAI) Infrastructure, allowing users to access, collect, and train AI models using community-sourced and validated data. This solution is designed for those who need access to high-quality data for AI model training, offering a scalable and cost-efficient way to tap into community-driven resources. Below is an overview of the Data Layer and how it empowers developers, businesses, and the community.
The Data Layer enables users to either train AI models on their own data or tap into the community to source high-quality datasets. Whether you're struggling to find the right data for your AI projects or seeking to reduce costs, this system offers a reliable solution by leveraging community efforts.
Train Your AI: Use the data you've collected or access community-contributed data to train advanced AI models.
To be among the first to access it, Join the Waiting List.
Crowdsourcing data collection and validation from the community is the most cost-effective and scalable way to gather large volumes of quality data. With the NeurochainAI Data Layer, the community plays an essential role in generating the data necessary to train AI models.
Save Costs: Crowdsource data to avoid the high costs associated with traditional data procurement.
Ensure Quality: All data collected is validated by the community, ensuring it's reliable and usable for AI tasks.
The NeurochainAI network offers a growing library of open-source AI models. Currently, five models based on popular architectures like Mistral, Vicuna, and Llama are available, with many more being added over time.
Ready-to-Use Models: These models are available for immediate use, and you can integrate them into your projects without needing to start from scratch.
Explore Available Models: Check out the range of AI models available for use on the network.
Not only can you use the models available on the NeurochainAI network, but you can also customize them to suit your specific needs. Tuning parameters such as context, temperature, and other variables allows you to tailor the models to your desired outputs.
Fine-Tune Your Models: Adjust parameters like context, temperature, and more, all with a simple request.
Custom Solutions: Create AI solutions that meet your unique requirements by leveraging the flexibility of the network's models.
