LM-Kit.NET

LM-Kit.NET

Supercharge .NET apps with innovative Gen. AI capabilities

5.0•1 review•

33 followers

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LM-Kit.NET is a high-level inference SDK for LLMs, offering advanced Generative AI capabilities for C# and VB.NET. It provides specialized AI functionalities, including text completion, NLP, content retrieval, text enhancement, translation, and more.
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Launch tags:API•Artificial Intelligence•SDK
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What do you think? …

Loïc Carrère
Maker
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We're thrilled to finally launch LM-Kit.NET! After countless hours of hard work, we've built a high-level inference SDK for Large Language Models that empowers developers with powerful AI capabilities for C# and VB.NET. We're excited to hear your feedback and see the impact it makes!
Martin Schurrer
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@minouch Hi Loic, that looks really exciting, congrats on the launch! I've been using Ollama recently, how does LM-Kit.NET compare to it?
Loïc Carrère
Maker
@msch thanks a lot for your question. When it comes to LM-Kit.NET, the focus is on providing .NET developers with deep integration and flexibility. LM-Kit.NET is delivered as a native .NET SDK, whereas Ollama is a container-based API that requires REST calls. While Ollama excels at quickly running LLMs, LM-Kit.NET offers a wide range of capabilities, from low to high-level inference, addressing the challenges developers may face when building Gen-AI-based functionalities. Today, I would argue that the three main features LM-Kit.NET offers, which Ollama does not, are structured output generation, function calling, and session management. We are continuously improving these features, and building them required significant R&D investment on our part. Now, our team is focused on identifying and developing new advanced capabilities leveraging Generative AI. We're also building a featured open source chat assistant, in MAUI (yeah we like challenges) that we're currently hosting here: https://github.com/LM-Kit/lm-kit... In terms of performance, LM-Kit.NET is optimized for high-volume processing, achieving up to 10x faster processing times and better accuracy in complex classification tasks like language detection, sentiment analysis, and custom classification. This is just our first iteration, and we're currently gathering feedback to see how early adopters respond. We're ready and eager to move forward with future updates which should be spectacular! Cheers, talk you soon. Loïc