Unveiling Synthetic LLM, a transformative feature in our Composable platform that is set to redefine the way tasks are distributed across multiple Large Language Models (LLMs) from diverse providers.
Synthetic LLM stands as a multiplexing powerhouse, enabling the distribution of tasks across various LLMs. The distribution is based purely on predefined weight assigned to each LLM, ensuring a straightforward and predictable load balancing system. They also automatically fail over to the next LLM in case of a task failure, ensuring consistent and reliable task execution.
Weight-Based Load Balancing: Tasks are allocated to different LLMs solely based on the weights defined by you. This method offers a clear and controlled approach to task distribution.
Robust Failover System: In case of a task failure on one LLM, Synthetic LLM automatically redirects it to the LLM with the next highest weight. This ensures consistent and reliable task execution.
The flexibility of Synthetic LLM opens up a plethora of applications:
Looking ahead, we plan to harness this approach and make Synthetic LLM even more powerful:
Best of Several: run tasks on multiple LLMs and then use a high-quality LLM to select the best output. This approach will not only enhance the decision-making process but also ensure the highest quality results for our users.
Dynamic Adjustment: dynamically adjust the priority of LLMs based on their performance. This will enable us to automatically route tasks to the best LLMs, ensuring the highest quality results for our users.
Stay connected for more exciting developments as we continue to push the boundaries of AI technology with Synthetic LLM. And don't hesitate to reach out to us, we'd love to hear your thoughts!