In this article, you will learn how to treat prompt templates as tunable hyperparameters for a language model, using scikit-learn’s grid search to find the best-performing prompt for a zero-shot text classification task.
Archive | Language Models
A Gentle Introduction to Model Distillation
In this article, you will learn what model distillation is, how it has evolved for large language models, and why it has become one of the most contested topics in the AI industry.
Versioning and Tracking Scikit-LLM Experiments
In this article, you will learn how to build, track, compare, and register scikit-learn pipelines that integrate large language models using Scikit-LLM and MLflow.
Combining LLM Embeddings with Tabular Features in a Unified Scikit-learn Pipeline
In this article, you will learn how to build a unified scikit-learn pipeline that combines text embeddings generated by a lightweight open-source language model with structured tabular features for classification tasks.
Interpretable Text Classification: Probing Scikit-LLM Embedding Spaces
In this article, you will learn how to use probing classifiers, UMAP visualization, and SHAP values to interpret and analyze the quality of text embeddings generated by large language models.
Comparing Local Tool Calling: Gemma 4 vs. Llama 3 vs. Mistral
In this article, you will learn how Gemma 4, Llama 3, and Mistral implement tool calling locally, and what trade-offs each model family presents for real-world deployment.
Managing Small Context Windows in Language Models
In this article, you will learn three practical strategies for managing small context windows in large language models, along with working Python examples that demonstrate how two of those strategies are implemented.
Prompt Caching vs. Fine-Tuning: A Cost and Latency Decision Framework
In this article, you will learn how prompt caching and fine-tuning differ as strategies for reducing cost and latency in agentic AI systems, and how to choose between them.
Ollama vs. LM Studio vs. llama.cpp: Which Local AI Runtime Should You Use in 2026?
Compare Ollama, LM Studio, and llama.cpp across five key dimensions to find the right local AI runtime for your workflow.
Run a Local AI Model with Ollama in 15 Minutes
In this article, you will learn how to get a small language model running locally on your own machine in under 15 minutes using Ollama.