Compare Ollama, LM Studio, and llama.cpp across five key dimensions to find the right local AI runtime for your workflow.
Archive | Language Models
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.
Scikit-Ollama for Scikit-LLM/Ollama Integration
In this article, you will learn how scikit-ollama bridges the scikit-learn interface with locally running Ollama models to perform zero-shot text classification; no cloud API required.
LLM Evaluation Frameworks Compared: How to Actually Measure What Your Model Does
In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why the LLM-as-a-judge mechanism they all rely on has measurable biases you need to actively design around.
LLM Orchestration Frameworks Compared: LangChain vs. LlamaIndex vs. Raw API Calls
In this article, you will learn how LangChain, LlamaIndex, and raw API calls each solve a different layer of the LLM application stack, and how to choose among them based on what your project actually requires.
Clustering Unstructured Text with LLM Embeddings and HDBSCAN
In this article, you will learn how to build a text clustering pipeline by combining large language model embeddings with HDBSCAN, a density-based clustering algorithm, to automatically discover topics in unlabeled text data.
Building an End-to-End Sentiment Analysis Pipeline with Scikit-LLM
In this article, you will learn how to build an end-to-end sentiment analysis pipeline using Scikit-LLM and open-source large language models served through the Groq API.
Multi-Label Text Classification with Scikit-LLM
Learn how to load, adapt, and leverage a pre-trained LLM for a multi-label classification task where a piece of text can be assigned one or multiple categories.
Multimodal Browser AI with Transformers.js for Images and Speech
In this article, you will learn how to build multimodal AI capabilities — image classification, image captioning, and speech transcription — that run entirely in the browser using Transformers.js, with no server, no API key, and no data leaving the user’s device.
Building Semantic Search with Transformers.js and Sentence Embeddings
In this article, you will learn how sentence embeddings work and how to build a fully client-side semantic search engine using Transformers.js, with no server, no API key, and no backend infrastructure required.