Учебные материалы по ИИ

Explore using Canopy as an open-source Retrieval Augmented Generation (RAG) framework and context built on top of the Pinecone vector database.

Discover how LangSmith optimizes LLM testing and debugging for AI applications. Enhance quality assurance and streamline development with real-world examples.

Most open-source LLMs have one huge drawback - short context length. Since context length is essentially the “memory” of LLMs, this issue needs to be addressed urgently. That’s exactly what Salesforce XGen 7B LLM does - provide an impressive 8k context length. This article is a tutorial on how to use and fine-tune it.

Learn how fine-tuning large language models (LLMs) improves their performance in tasks like language translation, sentiment analysis, and text generation.

Discover everything you need to know about LMQL, short for Language Models Query Language, an innovative programming language for LLMs.

Learn an end-to-end workflow to monitor any model in your Jupyter notebook in production environments.

Understanding the Phi-2 model and learning how to access and fine-tune it using the role-play dataset.

Step-by-step guide for building LLM-powered Chatbot on your own custom data, leveraging RAG techniques using OpenAI and Pinecone in Python.

Gemini defines a family of multimodal LLMs capable of understanding texts, images, videos, and audio. It’s also said to be capable of performing complex tasks in math and physics, as well as being able to generate high-quality code in several programming languages.

This tutorial dives into the Databricks approach to AI & Machine Learning in the Databricks Lakehouse and introduces its latest features.

A complete guide to using the SOLAR-10.7B fine-tuned model for instruction-based tasks in a real-world scenario.

Learn how to use Gemini Python API and its various functions to build AI-enabled applications for free.