Учебные материалы по ИИ

Prompt compression is the process of reducing the length of an input prompt while retaining the essential information needed for a language model to understand and generate a relevant response.

Learn how to deploy LLM applications using LangServe. This comprehensive guide covers installation, integration, and best practices for efficient deployment.

Discover the strengths of LLMs with effective information retrieval mechanisms. Implement a reranking approach and incorporate it into your own LLM pipeline.

Cohere offers powerful large language models for various language tasks through their user-friendly Playground or API.

We'll fine-tune Llama 3 on a dataset of patient-doctor conversations, creating a model tailored for medical dialogue. After merging, converting, and quantizing the model, it will be ready for private local use via the Jan application.

Run LLaMA 3 locally with GPT4ALL and Ollama, and integrate it into VSCode. Then, build a Q&A retrieval system using Langchain and Chroma DB.

Snowflake Arctic is a family of enterprise-grade language models designed to simplify the integration and deployment of AI within the Snowflake Data Cloud.

To connect to the Gemini 1.5 Pro API, obtain your API key from Google AI for Developers, install the necessary Python libraries, and send requests and receive responses from the Gemini 1.5 Pro model.

Explore how Power BI Copilot works. Learn about its features, pricing, and practical applications.

Explore how GitHub Copilot works with Visual Studio Code. Learn about its features, pricing, and practical applications for students and developers.

To connect through the GPT-4o API, obtain your API key from OpenAI, install the OpenAI Python library, and use it to send requests and receive responses from the GPT-4o models.

Batch normalization standardizes mini-batch inputs to stabilize and speed up neural network training.