In today's tech environment, the influence of large language models (LLMs) is profound and only growing. Despite this, many developers find themselves on the outskirts, unsure of where to start or how to integrate these AI powerhouses into their projects.
To help bridge this gap, there's an exciting new resource: The llm-api-starterkit. This accessible API Starter Kit enables developers to prototype an LLM-powered microservice with ease, providing a streamlined introduction to harnessing the full power of LLMs. The blog post below offers a simplified example of an LLM API deployment, designed to be accessible even for developers new to this realm, deliberately avoiding advanced concepts such as DevOps, MLOps, LLMOps, to keep the guide as straightforward as possible.
Check out Tim's blog post here:
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A stupidly minimal LLM API starterkit - Deploy LLM endpoints in a minute with LangChain & FastAPI. - timleers
In this post, I’ll help you build an LLM-powered microservice. You can follow along with my repository llm-api-starterkit. We’ll use two key
timleers
Remember, this is a bare-bones example of what's possible with LLMs. While the llm-api-starterkit doesn't dive into best practices and optimal software engineering principles, it's something that we at dataroots hold high for any of our projects, especially for deployment. That's also why we are actively conducting research on how to deploy LLMs at companies in the value chain in a safe and impactful manner.
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From Pandora’s Box to the Genie’s Lamp: Overcoming Challenges in Enterprise Adoption of Large-Language Models - Tim Leers
The race is on to harness the potential of Large Language Models (LLMs) in enterprises. Right now, there is significant risk in adopting LLMs in many usecases, without a clear path to deploying them to deliver business value. In part, that is because, the broad principles that drive value creation i…
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