Nimble Docs

Appearance

Data ​

Context ​

Just as an engine powers a car, data fuels AI models. The quality and specificity of data, much like the grade of gasoline, play a critical role in determining the performance of machine learning models. High-quality, well-prepared data enables GPUs to operate at peak efficiency, leading to better outcomes in tasks such as model training and predictive analysis.

However, the principle of "bigger is better" doesn't always apply when it comes to data. While the original Llama 70B was trained on 1 trillion tokens, which allows it to answer general questions, addressing more specific queries requires something akin to nitrous oxide (N2O) for cars: private, high-quality data. By integrating such specialized data and employing fine-tuning techniques, AI models can be precisely tailored to excel at specific tasks.

In essence, the quality and specificity of data are what shape the capabilities and effectiveness of AI models.

Nimble's Value ​

On-chain & Off-chain Data Availability ​

Understanding the critical role of data, Nimble has built strong connections with a diverse range of data providers from the start. Our network integrates both on-chain and off-chain sources, making datasets from sectors like GameFi, DeFi, and SocialFi easily trainable within our ecosystem. Furthermore, Nimble partners with traditional web2 data companies in healthcare, finance, and consumer industries to ensure a comprehensive data landscape.

Data Staking ​

Nimble incentivizes data providers to contribute their resources by allowing them to stake their data in exchange for Nimble tokens. This staking process both benefits our model training and offers additional rewards for high-quality data outputs.

Data Privacy and Security ​

Nimble employs advanced encryption and blockchain technology to ensure that all data remains secure and private. This approach safeguards the intellectual property of data providers and builds trust among end-users, ensuring that sensitive data is utilized responsibly while maintaining strict privacy and compliance standards.