Tweaking Local Language Model Settings with Ollama

KDnuggets•Thursday, May 28, 2026 at 2:00:17 PM

NeutralArtificial Intelligence

Tweaking Local Language Model Settings with Ollama

What Happened
The article delves into the configuration engine of Ollama, focusing on the intricacies of fine-tuning local language model parameters. This exploration aims to provide insights into optimizing the performance of language models tailored for specific applications.
Why It Matters
Understanding how to adjust these settings is crucial for developers and researchers in the AI field, as it can enhance the effectiveness of language models, leading to improved user experiences and more accurate outputs in various applications.

— via World Pulse Now AI Editorial System

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arXiv — cs.CL3 days ago

When Generic Prompt Improvements Hurt: Evaluation-Driven Iteration for LLM Applications

NeutralArtificial Intelligence

A recent technical report highlights the challenges of evaluating Large Language Model (LLM) applications, emphasizing that traditional software testing methods are inadequate due to the probabilistic and variable nature of LLM outputs. The report introduces the Minimum Viable Evaluation Suite (MVES), which aims to provide a structured approach to application-level evaluation, linking various metrics and validation evidence to specific application categories and failure modes.

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via arXiv — cs.CL

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