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Using Scikit-LLM with Open-Source LLMs11 hrs

This article will teach you how to perform a language task like text classification by integrating locally hosted large language models (LLMs) of manageable size, like Mistral, Gemma, and Llama 3: all for free thanks to Ollama — a free repository for local LLMs — and the Scikit-LLM Python library.

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Scikit-LLM vs. Traditional Text Classifiers: When Should You Use an LLM?3 days

In recent years, generative AI models like LLMs (large language models) have gradually taken over classical machine learning ones for addressing certain tasks, for instance, text classification .

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The Roadmap for Mastering LLMOps in 20264 days

The LLMOps market is projected to grow from

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Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient5 days

This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementation The simplest way to serve multiple requests together is to use static batching, by grouping them into fixed-size batches and processing each batch together.

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