IO Intelligence API provides a range of popular open-source large model services, including top-tier large language models, multimodal models, and embedding models. Its basic usage is compatible with the OpenAI SDK pattern, making it very easy to use. Below are some examples — just remember to replace the API key with your own.

Beyond basic image captioning, multimodal large models have many richer applications. Here are some common examples that are widely used in scenarios such as drones, robotic arms, and other applications.

Beyond basic image captioning, multimodal large models have many richer applications. Here are some common examples that are widely used in scenarios such as drones, robotic arms, and other applications.

ionet's agents are predefined AI intelligent agents that integrate specialized instructions, extended knowledge bases, and multiple skills into one.

They mainly include commonly used Agents such as translation, classification, summarization, etc.

Here we continue to introduce some relatively complex agents and custom agents.

We can think of it as transforming Large Language Models (LLMs) from "**closed-book exams**" to "**open-book exams**".

Introducing Knowledge Graphs into RAG systems is like equipping the "reference book" in our previously mentioned "open-book exam" with a **super-intelligent table of contents and indexing system**, or even a **GPS navigation map for knowledge**.

Below is a brief introduction to knowledge graph applications in RAG.

Here we demonstrate the method of building a RAG system from scratch. The basic workflow is as follows:

Here's a brief introduction to "**Agentic Workflow**" (Intelligent Agent Workflow).

IO Intelligence Agent is a complete, agent-oriented programming framework, similar to code agent frameworks like SmolAgent. It supports MCP (Multi-Capability Protocol) and provides very generous free quotas, making it an excellent choice for learning agent programming.

Ionet provides model training services in the form of SAAS, which is Training as a Service (TaaS). You can use your own data to fine-tune existing models to achieve customized performance without training from scratch.

`io.net Cloud` provides a series of flexible deployment options designed to meet different AI computing needs from individual developers to enterprise-level users.

Ionet's virtual machines are similar to general AWS ECS servers, etc., and are the most commonly used type of server rental method.

For large model applications, they come pre-installed with frameworks like PyTorch and CUDA, and can directly run most machine learning code.

The specific rental process is similar to AWS and other cloud platforms. Here we mainly explain some key points.