Open Source Models

Cortensor leverages open-source models to provide robust and flexible AI inference capabilities. By utilizing these models, Cortensor ensures that the network remains accessible, transparent, and adaptable to various use cases.

Supported Models

Llama 3:

• Available in both quantized and regular versions.

• Supports a wide range of hardware, from low-end devices to high-end GPUs.

• Enables broad participation in AI inference tasks by accommodating diverse computational resources.

• Quantization allows lower-end devices to perform inference tasks, promoting inclusivity and scalability.

Future Plans

• Expansion of Llama 3 Models: Cortensor plans to add more variations of Llama 3-based models to enhance the network’s capabilities and provide greater flexibility for different tasks.

• Integration of Additional Open Source Models: Beyond Llama 3, Cortensor is committed to integrating other open-source AI models. This will further diversify the network’s capabilities and ensure it remains at the forefront of AI technology.

Benefits of Open Source Models

• Transparency: Open-source models allow for greater transparency and trust within the network, as their development and updates are publicly available.

• Community-Driven Innovation: Leveraging open-source models encourages community contributions and collaboration, driving continuous improvement and innovation.

• Cost-Effectiveness: Open-source models reduce the cost barriers for implementing advanced AI capabilities, making AI inference more accessible to a broader audience.

• Flexibility: The use of open-source models ensures that Cortensor can adapt to new advancements and integrate various AI technologies as they evolve.

• Quantization: Model quantization enables lower-end devices to participate in AI inferencing, enhancing the network’s inclusivity and resource utilization.