Учебные материалы по LLM

Learn to build a RAG application with Llama 3.1 8B using Ollama and Langchain by setting up the environment, processing documents, creating embeddings, and integrating a retriever.

Learn how to fine-tune Meta AI's SAM 2 using the Chest CT Segmentation dataset to improve the model's image segmentation performance in medical image analysis.

Learn how to fine-tune TimeGPT, the first foundational model for time series datasets, for forecasting and anomaly detection with just a few lines of code.

Learn how to fine-tune LLMs on custom datasets, evaluate performance, and seamlessly export and serve models using the LLaMA-Factory's low/no-code framework.

RankGPT is a method that uses LLMs like ChatGPT to re-rank retrieved documents in RAG systems, improving the quality of the output by prioritizing the most relevant information.

A comprehensive tutorial on building multi-tool LangChain agents to automate tasks in Python using LLMs and chat models using OpenAI.

Get started with Apple's DCLM-7B large language model and learn how to set it up, use it, and fine-tune it for specific tasks.

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.

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

Learn how OpenAI's new Function Calling capability enables GPT models to generate structured JSON output, resolving common dev issues caused by irregular outputs.