LLM 教程

Customize the GPT-4o Mini model to classify posts from Reddit into "stressful" and "non-stressful" labels.

Learn how to access Llama 3.2 lightweight and vision models on Kaggle, fine-tune the model on a custom dataset using free GPUs, merge and export the model to the Hugging Face Hub, and convert the fine-tuned model to GGUF format so it can be used locally with the Jan application.

Learn how to use Mistral’s Pixtral 12B interactively via Le Chat or programmatically through the API available on La Plateforme.

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.