LLM チュートリアル

Learn to build AI chatbots with Streamlit, LangChain, and Neo4j. This tutorial covers creating UIs for LLM apps, implementing RAG, and deploying to Streamlit Cloud.

Learn how to build real-time AI applications with OpenAI's Realtime API. This tutorial covers WebSockets, Node.js setup, text/audio messaging, function calling, and deploying a React voice assistant demo.

Discover Microsoft's new LLM series and boost your model's accuracy from 65% to 86% by fine-tuning it on the E-commerce classification dataset.

Learn Speculative RAG, a technique that improves RAG through a two-step drafting and verification process, and apply your skills with a hands-on implementation using Hugging Face Transformers.

Fine-tuning the Llama 3.1 model to solve specialized algebra problems with high accuracy and detailed results using Unsloth.

Learn how to fine-tune GPT-4o with images by following this step-by-step tutorial, where you’ll discover how to improve GPT-4o’s ability to recognize Georgian churches.

Learn how to use the Llama 3.2 11B vision model with Gradio to create a multimodal web app that functions as a customer support assistant.

Learn how to distill LLMs with OpenAI's distillation tool. This tutorial provides a step-by-step guide using GPT-4o and GPT-4o-mini for generating Git commands.

Llama Stack is a set of standardized tools and APIs developed by Meta that simplifies the process of building and deploying large language model applications.

Customize the GPT-4o Mini model to classify posts from Reddit into "stressful" and "non-stressful" labels.

Learn how to access Llama 3.2 lightweight and vision models on Kaggle, fine-tune the model on a custom dataset using free GPUs, merge and export the model to the Hugging Face Hub, and convert the fine-tuned model to GGUF format so it can be used locally with the Jan application.

Learn how to use Mistral’s Pixtral 12B interactively via Le Chat or programmatically through the API available on La Plateforme.