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The Best Lightweight LLMs of 2025: Efficiency Meets Performance

ODSC - Open Data Science

Best Use Cases: Research, AI-powered documentation tools, and knowledge retrieval. Weaknesses: Requires precise prompt engineering for optimalresults. TinyLlama & Phi-3Mini Overview: These ultra-lightweight models focus on mobile and embedded AI applications.

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A Guide to Mastering Large Language Models

Unite.AI

Prompting Rather than inputs and outputs, LLMs are controlled via prompts – contextual instructions that frame a task. Prompt engineering is crucial to steering LLMs effectively. Embeddings Word embeddings represent words as dense vectors encoding semantic meaning, allowing mathematical operations.

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Evaluation Derangement Syndrome (EDS) in the GPU-poor’s GenAI. Part 1: the case for Evaluation-Driven Development

deepsense.ai

In short, EDS is the problem of the widespread lack of a rational approach to and methodology for the objective, automated and quantitative evaluation of performance in terms of generative model finetuning and prompt engineering for specific downstream GenAI tasks related to practical business applications. Galstyan A. Cresswell J.C.,