Evals Expanding to Third Party Models & Agent Testing

Latent Space · Intermediate ·🤖 AI Agents & Automation ·9mo ago

Key Takeaways

Evals is expanding to support third-party models and agent testing, allowing users to integrate non-open models and multimodels into their workflows, with a specific setup using Open Router for evaluation and testing across multiple model providers.

Full Transcript

But one of the things that we launched today with evals too is ability to use like thirdparty models as well and kind of bringing that in to one place. And so I think definitely kind of see where the ecosystem is at which is you know using multimodels and kind of having >> third party models as in non nonopen models. >> Yeah. Yeah. It'll work with eval starting today. >> Yeah. >> Okay. Got it. Uh we have a really cool setup with open router where we're working with them and then you can bring your open router set up and then with that you can actually you know you write your evals using our data sets tool or use our data set tool to create a bunch of evals and you'd actually be able to hit a bunch of different model providers um you know take your pick from wherever even like open source ones on together and see the see the results uh in our
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The video discusses the expansion of Evals to support third-party models and agent testing, enabling users to evaluate and compare model performance across multiple providers. This feature allows for more flexibility and customization in model evaluation and testing. By using Evals and Open Router, users can create evaluations and test models from various providers, including non-open and open-source models.

Key Takeaways
  1. Set up Open Router with Evals
  2. Create evaluations using Evals data sets tool
  3. Integrate third-party models into Evals
  4. Configure model providers for testing
  5. Run evaluations and compare model performance
💡 The ability to integrate third-party models and multimodels into Evals enables more comprehensive model evaluation and testing, allowing users to make more informed decisions about model performance and selection.

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