The article discusses the deployment challenges of Large Language Models (LLMs) on edge devices due to memory and speed limitations. It highlights the development of smaller models like Gemma-2B and Llama-7B, along with frameworks such as MLC LLM that support operation on devices. Furthermore, it reviews recent advancements in function calling by smaller models, presenting various projects that demonstrate their effectiveness in API integration, even achieving results comparable to larger models like GPT-4. This indicates a significant trend in LLM capabilities and adaptability for real-world applications.
The advancements in deploying smaller-scale Large Language Models (LLMs) on edge devices face challenges like memory limitations but initiatives like MLC LLM allow compatibility across various hardware.
Projects such as NexusRaven, Toolformer, and others have shown that 7B and 13B models can effectively call external APIs, competing with the capabilities of GPT-4.
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