Tutorials

AI Model Hub for Free: From December 1, 2024, to June 30, 2025, IONOS is offering all foundation models in the AI Model Hub for free. Create your contract today and kickstart your AI journey!

The IONOS AI Model Hub offers powerful AI capabilities to meet various needs. Here are six pivotal tutorials on how you can implement the features of this service:

Prerequisite: Authentication Tokens

The IONOS AI Model Hub uses authentication tokens to ensure that only users with corresponding permissions can make use of it.

Key Features

• Authentication tokens are bound to IONOS Public Cloud users.

• Usage is billed via the IONOS Public Cloud contract owner responsible for these users.

The Access Management tutorial includes step-by-step instructions for generating an IONOS Public Cloud contract, users, and authentication tokens.

Tutorial 1: Text Generation

Text generation models enable advanced language processing tasks, such as content creation, summarization, conversational responses, and question-answering. These models are pre-trained on extensive datasets, enabling high-quality text generation with minimal setup.

Key Features

• Access open-source Large Language Models (LLMs) via an OpenAI-compatible API.

• Ensure data privacy with processing confined within Germany.

For step-by-step instructions on text generation, see the Text Generation tutorial.

Tutorial 2: Image Generation

Image generation models allow you to create high-quality, detailed images from descriptive text prompts. These models can be used for applications in creative design, marketing visuals, and more.

Key Features

• Generate photorealistic or stylized images based on specific prompts.

• Choose from models optimized for authenticity or creative and artistic outputs.

To learn how to implement image generation, see the Image Generation tutorial.

Tutorial 3: Text Embeddings

Embedding models allow you to create numerical representations of texts, which are similar if the texts are semantically similar. These models are ideal for applications like text retrieval, comparison, ranking, etc.

Key Features

• Identify texts that answer a query based on semantic similarity between the query and the potential answer.

• Compare texts to determine their semantic closeness or difference.

To learn how to derive embeddings and calculate the similarity of texts, see the Text Embeddings tutorial.

Tutorial 4: Document Collections

Vector databases enable you to store and query large collections of documents based on semantic similarity. Converting documents into embeddings allows you to perform effective similarity searches, making it ideal for applications like document retrieval and recommendation systems.

Key Features

• Persist documents and search for semantically similar content.

• Manage document collections through simple API endpoints.

For detailed instructions, see Document Collections tutorial.

Tutorial 5: Retrieval Augmented Generation (RAG)

RAG combines the strengths of foundation models and vector databases. It retrieves the most relevant documents from the database and uses them to augment the output of a foundation model. This approach enriches the responses, making them more accurate and context-aware.

Key Features

• Combine foundation models with additional context retrieved from document collections.

• Enhance response accuracy and relevance for user queries.

To learn how to implement Retrieval Augmented Generation, see the Retrieval Augmented Generation tutorial.

Tutorial 6: Tool Integration

The IONOS AI Model Hub can be seamlessly integrated into various frontend tools that use Large Language Models or text-to-image models through its OpenAI-compatible API. This integration allows you to leverage foundation models in applications without complex setups. For example, using the tool AnythingLLM, you can configure and connect to the IONOS AI Model Hub to serve as the backend for Large Language Model functionalities.

Key Features

• Easily connect to third-party tools with the OpenAI-compatible API.

• Enable custom applications with IONOS-hosted foundation models.

For detailed guidance on integrating with tools, see the Tool Integration tutorial.

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These tutorials will guide you through the basic functionality of the AI Model Hub, providing clear and actionable steps to integrate advanced AI capabilities into your applications. If you are interested in advanced use cases and how to implement them using the described functionality, see our use cases