Upgrading WatsonX.ai BYOM Support for T5 Models with vLLM Runtime

Why Is It Useful?

The watsonx.ai BYOM runtime v1.0 current reliance TGIS runtime limits WatsonX.ai's ability to support critical features like beam search, essential for improved output quality in applications such as machine translation. 

Customers with T5 models have no support for critical functionality such as "beam search" which is currently required by some customers as it supports better output quality in scenarios like machine translation. 

vLLM is an open-source runtime which already has support for these features which is already available for other model architectures using runtime v1.1, however BYOM for T5 architecture have not been upgraded to v1.1 with vLLM as runtime.

Upgrading the BYOM solution for T5 architecture to use vLLM ensures compatibility with modern NLP tasks and maintains competitiveness by offering feature parity with open-source solutions. 

Who Would Benefit from It?

• Current and future WatsonX.ai Users: Those relying on T5 models for advanced NLP applications, particularly in machine translation where beam search is proved to be vital.
• Competitive Edge: Customers expecting cutting-edge features will appreciate the upgrade, enhancing their decision to choose WatsonX.ai over competitors.
• IBM Sellers: This being a blocker for selling watsonx.ai solutions for potential customer who plan or rely on deploying T5 models.

How Should It Work?

1. Runtime Upgrade: Transition from runtime v1.0 and TGIS to runtime v1.1 with vLLM integration.
2. Beam Search Implementation: Ensure beam search is included for feature parity, enhancing output quality in translation tasks. This should include native support for beam search possibly as custom inference parameters or runtime parameters.
3. Collaboration & Testing: Engage the community for feedback and testing, ensuring seamless compatibility and performance.

This upgrade addresses a critical gap, positioning WatsonX.ai as a leader in NLP solutions while mitigating potential revenue loss from lost customers and deals.