Hivetrain Roadmap
Here's an outline of our roadmap.
What We're Doing
Why It Matters
Status
1
Utilizing Distributed Training with FSDP
Efficient distributed training is key for scaling AI models.
FSDP allows for training larger models by optimizing
resource use, which is critical for democratizing access to
cutting-edge AI.
Code complete - testing: Enhancing our training framework
for efficient, scalable AI development.
2
Incorporating Security and Integrity Measures
In a decentralized environment, security and integrity are
paramount for maintaining trust and ensuring that
contributions enhance model quality without compromising
data privacy.
Security Measures: Implementing cryptographic signing and
validation to secure the training process.
3
Adapting to Dynamic World Sizes in Distributed Training
Flexibility in training dynamics ensures that changes in
network participation don't hinder the model development
process, allowing for continuous progress regardless of
scale.
Dynamic Adaptation: Regular updates to training
configurations for optimal performance.
4
Building the Foundation for Open-Source AI Development
The proprietary nature of most large AI models limits their
utility and accessibility. By focusing on open-source
models, we ensure that AI's benefits can extend across
industries and communities.
Open-Source Commitment: Prioritizing the development and
sharing of models to foster widespread AI innovation.
5
Freechat (coming soon!)
Best available open models, hosted and served in a
distributed manner, allowing you to access and use it
without cost.
Development in progress.