Edge 391: Our series about autonomous agents continues with the fascinating topic of function calling. We explore UCBerkeley's research on LLMCompiler for function calling and we review the PhiData framework for building agents. Last year, Microsoft coined the term 'small language model' (SLM) following the publication of the influential paper 'Textbooks Are All You Need', which introduced the initial Phi model. Since then, there has been a tremendous market uptake in this area, and SLMs are starting to make inroads as one of the next big things in generative AI. The case for SLMs is pretty clear. Massively large foundation models are likely to dominate generalist use cases, but they remain incredibly expensive to run, plagued with hallucinations, security vulnerabilities, and reliability issues when applied in domain-specific scenarios. Add to that environments such as mobile or IoT, which are computation-constrained by definition. SLMs are likely to fill that gap in the market...
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