【ローカルAI時代】Ollama（オラマ）の導入・使い方&Codexで専用チャットアプリ作ってみた！SSL（小規模言語モデル）の利点も解説（使用オープンモデル：Gemma4,スワロー）

[Related Videos] 👇Explaining how to build a safer and easier local AI agent loop with Ollama than Openclow (generating a dedicated chat with Codex)    • 「Ollama（オラマ）」でOpenclowより安全・簡単なAIエージェントループ...   👇[AI Development] Implementing inference with AI coding in Ollama's local AI using Codex!    • 【AI開発】CodexでローカルAIにAIコーディングで推論を実装！Ollama（オ...   👇A thorough explanation of context-first approach to break the limits of "local AI" and "small language models (SLM)" with Ollama!    • 「Ollama（オラマ）」で「ローカルAI」「小規模言語モデル（SLM）」に必須...   👇Explaining the amazing mechanism of Gemma 4 (E4B)! The Dawn of the Local AI/Edge AI Era (Tested on a MacBook Air M5 16GB)    • Gemma 4（E4B）がなぜ凄いのか仕組みを解説！思考能力をブーストするプロンプト...   [Download] 👇Download page for the Ollama-dedicated app "AI Co-creation Local Nova" https://github.com/AICo-Creation/loca... *This link will take you to the GitHub page. 👇AI Co-creation Local Nova explanation page https://aico-creation.github.io/local... *See the latter half of this page for instructions on how to launch "AI Co-creation Local Nova" (a command is required the first time you use it). ■ The Cutting Edge of Local AI: Building and Implementing Next-Generation Environments with Ollama Local AI, which allows large-scale language models to run completely offline on your own PC, is rapidly gaining popularity from the perspective of protecting corporate data privacy and reducing API costs. This explanation comprehensively unravels everything from the architecture of Ollama, the core inference platform, to specific implementation methods. You can break free from reliance on cloud APIs, maximize the potential of your own hardware, and deepen your knowledge to build a secure AI foundation. ▫️Security Crisis and the Arrival of the Local AI Era With the emergence of Claude Mythos, which is highly resistant to cybersecurity attacks, and the comparable GPT 5.5, a local environment has become essential. However, people still want to use AI, and that's where the local AI platform Ollama comes in. By downloading open models with Ollama and disconnecting from the internet, a completely local environment is achieved. This makes it possible to use AI safely and securely. ★ Innovation in Local Environments Brought About by Small Language Models (SLMs) ★ The biggest advantage of small language models, which consist of billions to tens of billions of parameters, is that they run smoothly even with the limited hardware resources of typical consumer PCs and laptops. Models in the 7B to 8B class can perform inference sufficiently in an 8GB to 16GB RAM and VRAM environment, and when combined with quantization technology, they enable text generation with extremely low latency. This makes it possible to perform advanced data processing and coding assistance quickly and securely in a personal environment without relying on expensive cloud servers. The video explains the advantages of small-scale language models over LLMs, such as low noise, specialization, and RAG (Random Aggregation) capabilities. ◆ Open Models Ideal for Practical Application: Gemma4 and Swallow ◆ As cutting-edge open models that truly shine in local environments, we dissect the architectures of Gemma4 and Llama 3 Swallow, developed by Tokyo Institute of Technology. Gemma4 is a powerful model with advanced inference capabilities and multimodal understanding, particularly optimized for coding and autonomous agent workflows. Llama 3 Swallow, on the other hand, is based on Meta's Llama 3 and undergoes continuous pre-training with a vast Japanese corpus, boasting extremely high accuracy and reliability in understanding Japanese-specific contexts and specialized business nuances. ▼ Ollama Installation Process and Practical Usage ▼ Ollama provides a Docker-like command-line interface familiar to software engineers, allowing you to complete everything from model download to execution with a single command. It natively supports Windows, macOS, and Linux platforms, automatically detecting the installed GPU resources to optimize model performance. Furthermore, by creating a configuration file (Modelfile), you can fine-tune system prompts and inference parameters for the base model, easily building custom AI with unique personas. ▫️Temperature Settings Ollama's default temperature setting is a high 0.8; NVIDIA recommends a lower setting when using RAG with Nemotoron. High temperatures increase the likelihood of hallucination. Therefore, learning about local AI and SLM is necessary. ▫️Physical AI, Edge AI, and Small-Scale Language Models The video suggests that physical AI and edge AI may also become local AI/SLM in the future from a privacy protection perspective. The peace of mind of complete offline privacy protection is important for the lives of ordinary people. ■ Autonomous Development of a Dedicated Chat App with Codex ■ Ollama is more than just an inference engine; it boasts an ecosystem that enables seamless integration with various AI coding agents. Here, we demonstrate a dedica...