Use the powerful and extensible LangChain framework, using prompts, parsing, memory, chains, question answering, and agents.

Learn and build diffusion models from the ground up, understanding each step. Learn about diffusion models in use today and implement algorithms to speed up sampling.

Learn to break down complex tasks, automate workflows, chain LLM calls, and get better outputs from LLMs. Evaluate LLM inputs and outputs for safety and relevance.

Create and demo machine learning applications quickly. Share your app with teammates and beta testers on Hugging Face Spaces.

Learn MLOps tools for managing, versioning, debugging, and experimenting in your ML workflow.

Learn about the latest advancements in LLM APIs and use LangChain Expression Language (LCEL) to compose and customize chains and agents.

Get an introduction to tuning and evaluating LLMs using Reinforcement Learning from Human Feedback (RLHF) and fine-tune the Llama 2 model.

Learn best practices for prompting and selecting among Meta Llama 2 & 3 models. Interact with Meta Llama 2 Chat, Code Llama, and Llama Guard models.

Learn how to easily build AI applications using open-source models and Hugging Face tools. Find and filter open-source models on Hugging Face Hub.

Understand how LLMs predict the next token and how techniques like KV caching can speed up text generation. Write code to serve LLM applications efficiently to multiple users.

Learn how to make safer LLM apps through red teaming. Learn to identify and evaluate vulnerabilities in large language model (LLM) applications.

Learn how to quantize any open-source model. Learn to compress models with the Hugging Face Transformers library and the Quanto library.

Explore Mistral's open-source and commercial models, and leverage Mistral's JSON mode to generate structured LLM responses. Use Mistral's API to call user-defined functions for enhanced LLM capabilities.

Learn prompt engineering for vision models using Stable Diffusion, and advanced techniques like object detection and in-painting.

Build agentic AI workflows using LangChain's LangGraph and Tavily's agentic search.

Learn Python programming with AI assistance. Gain skills writing, testing, and debugging code efficiently, and create real-world AI applications.

Learn best practices for multimodal prompting using Google’s Gemini model.

Build faster and more relevant vector search for your LLM applications

Try out the features of the new Llama 3.2 models to build AI applications with multimodality.

Efficiently handle time-varying workloads with serverless agentic workflows and responsible agents built on Amazon Bedrock.

Build agents that collaborate to solve complex business tasks.

Learn to build with LLMs by creating a fun interactive game from scratch.

Build LLM apps that can process very long documents using the Jamba model

Learn how an AI Assistant is built to use and accomplish tasks on computers.

Build multimodal and long-context GenAI applications using Llama 4 open models, API, and Llama tools.

Adapt LLMs for specific tasks and behaviors using post-training techniques like SFT, DPO, and online RL.

Build reliable LLM applications with structured outputs and validated data using Pydantic.