Учебные материалы по ИИ

Few-shot prompting is a technique in which an AI model is given a few examples of a task to learn from before generating a response, using those examples to improve its performance on similar tasks.

Learn how to convert technical models into interactive user interfaces with Gradio in Python.

Learn the differences between RAG and Fine-Tuning techniques for customizing model performance and reducing hallucinations in LLMs.

Qwen is a family of large language and multimodal models developed by Alibaba Cloud, designed for various tasks like text generation, image understanding, and conversation.

Unlock the full potential of Large Language Models (LLMs) with our guide on Chain-of-Thought (CoT) prompting. Learn how to enhance reasoning and problem-solving abilities in LLMs.

This article compares the performance of MatMul-free and traditional large language models in terms of speed and memory usage.

This article presents a practical guide to implementing TNT-LLM, a framework that automates taxonomy generation and text classification using LLMs, demonstrated through a news article clustering example with GPT-4 and LangGraph.

Prompt chaining is a technique that involves breaking down a complex task into a series of smaller, interconnected prompts, where the output of one prompt serves as the input for the next, guiding the LLM through a structured reasoning process.

This tutorial guides you through DuckDB's key features and practical applications, including building tables, performing data analysis, building an RAG application, and using an SQL query engine with LLM.

Explore Cohere Command R+ online and locally, learn about the unique features of the Cohere Python API, and build a multi-step AI agent using LangChain and Cohere.

A Comprehensive Guide to Reducing Model Sizes

To connect through the Claude 3.5 Sonnet API, obtain your API key from Anthropic, install the anthropic Python library, and use it to send requests and receive responses from Claude 3.5 Sonnet.