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Startup Field Guide by Unusual Ventures: The Product Market Fit Podcast
How open source AI will find product market fit: A conversation with Databricks, and AI startup Together
Sep 25, 2023
Founders of Databricks and Together discuss the rise of open source AI, building with open source, standardization of LLMs, the role of academia in AI research, innovations in training data, growing accessibility of machine learning with LLMs, the future of the open source ecosystem, and best practices for parameterizing LLMs.
46:33
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Quick takeaways
- When using open source models, start with smaller models and gradually try different ones that align with your application goals and performance requirements.
- Continuously monitor and improve models by evaluating and experimenting with different models, and leveraging established practices from applied machine learning.
Deep dives
Choosing and Experimenting with Open Source Models
When starting with open source models, it is important to consider the size of the model and how it aligns with your application goals and performance requirements. You can start with smaller models and gradually try different models that rank well on the parameter count. There are resources available, such as Databricks' webpage that recommends best-in-class open source generated AI models for different use cases.
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