LLM チュートリアル

Master the Ollama Python SDK for local LLM development. Learn to generate text, handle multi-turn chats, use vision models, and build AI apps securely.

Discover how to install and run LLMs locally using LM Studio. Keep your data private, chat with documents using built-in RAG, and set up a local API.

Learn how to run PrismML's fast and efficient 1-bit Bonsai model locally on Windows, from download and setup to WebUI testing, reaching 88.6 tokens per second.

Learn how to run GLM 5.1 locally on an H100 GPU with llama.cpp, test it, use the WebUI, and integrate Claude Code.

Learn how to run Meta's TRIBE V2 model on Google Colab, predict cortical activity from naturalistic stimuli, and visualize the results as interactive 3D brain heatmaps.

Discover how Gemini Embedding 2 has revolutionized the way we can build search and RAG systems by introducing native multimodality. Text, video, images, and audio can now all live in the same embedding space.

Control your desktop AI agent from your phone with Claude Cowork Dispatch. Learn how to assign tasks and automate file workflows remotely in this guide.

Learn how to install and configure claude-mem, the Claude Code plugin that gives your sessions persistent memory through structured compression and retrieval.

Set up vLLM on a H100 GPU, serve Qwen3.5-27B, connect OpenCode, and test fast agentic coding with long context support.

Learn how to use NVIDIA’s Nemotron 3 Super through OpenRouter to analyze a GitHub repository without RAG, using prompt packing and a Gradio app.

Learn how to fine-tune Qwen3.5-9B on the Latex OCR dataset using QLoRA in Unsloth Studio, then test the model in chat and export it in GGUF format for local inference.

Build reliable AI apps faster by turning ad-hoc prompt checks into structured LLM evaluations with Promptfoo, from local test suites to automated CI.