LLM 教程

Learn how to use Flux AI to generate images and explore the features, applications, and use cases of each model in the Flux family: Flux Pro, Flux Dev, and Flux Schnell.

Learn how to make the most of NotebookLM by utilizing its podcast-generating feature and its ability to organize, summarize, and answer questions about your documents.

Corrective RAG (CRAG) is a RAG technique that incorporates self-assessment of retrieved documents to improve the accuracy and relevance of generated responses.

LangGraph Studio is a visual development environment for LangChain’s LangGraph framework, simplifying the development of complex agentic applications built with LangChain components.

Learn how to use OpenAI o1 to build an end-to-end machine learning project from scratch using just one prompt.

Reflection Llama-3.1 70B, trained with Reflection-Tuning, claims to surpass GPT-4o and Claude 3.5 Sonnet but has faced reproducibility and verification issues so far.

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.