SE Radio 661: Sunil Mallya on Small Language Models

Sunil Mallya, co-founder and CTO of Flip AI,
discusses small language models with host Brijesh Ammanath. They
begin by considering the technical distinctions between SLMs and
large language models. 


LLMs excel in generating complex outputs across various natural
language processing tasks, leveraging extensive training datasets
on with massive GPU clusters. However, this capability comes with
high computational costs and concerns about efficiency,
particularly in applications that are specific to a given
enterprise. To address this, many enterprises are turning to
SLMs, fine-tuned on domain-specific datasets. The lower
computational requirements and memory usage make SLMs suitable
for real-time applications. By focusing on specific domains, SLMs
can achieve greater accuracy and relevance aligned with
specialized terminologies.


The selection of SLMs depends on specific application
requirements. Additional influencing factors include the
availability of training data, implementation complexity, and
adaptability to changing information, allowing organizations to
align their choices with operational needs and constraints.


This episode is sponsored by Codegate.