NashTech Blog

Author name: naman_jain

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Application Engineering, Applied AI & ML

Use Local LLM With Ease

Local Language Model (LLM) use is driven by the latest AI trends, with renowned platforms such as Open AI, Azure, and Antropedia embracing open API access. While these LLMs offer exceptional capabilities, they also present challenges like cost, inflexibility, security, and dependency. LM Studio addresses these issues, allowing easy integration of LLMs on various devices without intricate configurations. Users can select custom LLMs from Hugging Face and utilize them through chat interfaces and APIs. Furthermore, LM Studio seamlessly integrates with AI orchestrators like Semantic Kernel and Langchain, enhancing its versatility and functionality for diverse AI applications. Ollama, an alternative open-source tool, also provides robust support for LLMs. Overall, local LLMs offer substantial potential for testing and production, enabling the utilization of diverse custom LLMs tailored to specific needs.

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Application Engineering, Applied AI & ML

Super Charge Your App with AI using Semantic Kernel

Large Language Models (LLMs) trained on vast text datasets have revolutionized AI applications. However, they are challenged by scenarios that require nuanced, multi-step responses. The Semantic Kernel facilitates effective AI orchestration by managing AI tools like Semantic Memory and History. It offers numerous features such as prompt templating, functions, planners, and persona abilities. Supported by pre and post hooks, the tool accepts, manages, and responds effectively to user inputs, fostering efficient AI application development. An application integrating a Semantic Kernel can easily manage AI tools in a clean, user-friendly manner.

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